@prefix dbpedia-owl: .
@prefix dbpedia: .
dbpedia-owl:genre dbpedia:Machine_learning .
@prefix ns2: .
ns2:genre dbpedia:Machine_learning .
@prefix dbpprop: .
dbpprop:genre dbpedia:Machine_learning .
dbpedia:Machine_learning_algorithm dbpprop:redirect dbpedia:Machine_learning .
dbpedia:Journal_of_Machine_Learning_Research dbpprop:discipline dbpedia:Machine_learning .
dbpedia:Pierre_Baldi dbpedia-owl:knownFor dbpedia:Machine_learning .
@prefix ns4: .
dbpedia:Pierre_Baldi ns4:knownFor dbpedia:Machine_learning ;
dbpprop:knownFor dbpedia:Machine_learning .
@prefix rdf: .
@prefix opencyc: .
dbpedia:Machine_learning rdf:type opencyc:Mx4rsptUGhfiQdmQ1dIv6htA7Q .
@prefix owl: .
dbpedia:Machine_learning owl:sameAs .
@prefix ns8: .
dbpedia:Machine_learning owl:sameAs ns8:Mx4rgBzbIHYqQdmfo5QMiMv_Hw ,
opencyc:Mx4rgBzbIHYqQdmfo5QMiMv_Hw .
@prefix foaf: .
@prefix ns10: .
dbpedia:Machine_learning foaf:page ns10:Machine_learning ;
dbpprop:reference ,
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@prefix ns11: .
dbpedia:Machine_learning dbpprop:reference ns11:Encyclopedia_of_Computational_Intelligence ,
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@prefix rdfs: .
dbpedia:Machine_learning rdfs:label "Machine learning"@en ,
"\u041C\u0430\u0448\u0438\u043D\u043D\u0435 \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u044F"@uk ,
"Strojov\u00E9 u\u010Den\u00ED"@cs ,
"Makine \u00F6\u011Frenimi"@tr ,
"Aprendizaje autom\u00E1tico"@es ,
"Machinaal leren"@nl ,
"Uczenie maszynowe"@pl ,
"\u6A5F\u68B0\u5B66\u7FD2"@ja ,
"Apprentissage automatique"@fr ,
"Koneoppiminen"@fi ,
"Apprendimento automatico"@it ,
"\u041C\u0430\u0448\u0438\u043D\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435"@ru ,
"Maschinelles Lernen"@de ,
"Maskininl\u00E4rning"@sv ,
"Aprendizagem de m\u00E1quina"@pt ,
"Maskinl\u00E6ring"@no ,
"Aprenentatge autom\u00E0tic"@ca ,
"\u673A\u5668\u5B66\u4E60"@zh ;
dbpprop:abstract "\u041C\u0430\u0448\u0438\u043D\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u2014 \u043E\u0431\u0448\u0438\u0440\u043D\u044B\u0439 \u043F\u043E\u0434\u0440\u0430\u0437\u0434\u0435\u043B \u0438\u0441\u043A\u0443\u0441\u0441\u0442\u0432\u0435\u043D\u043D\u043E\u0433\u043E \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u0430, \u0438\u0437\u0443\u0447\u0430\u044E\u0449\u0438\u0439 \u043C\u0435\u0442\u043E\u0434\u044B \u043F\u043E\u0441\u0442\u0440\u043E\u0435\u043D\u0438\u044F \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C\u043E\u0432, \u0441\u043F\u043E\u0441\u043E\u0431\u043D\u044B\u0445 \u043E\u0431\u0443\u0447\u0430\u0442\u044C\u0441\u044F. \u0420\u0430\u0437\u043B\u0438\u0447\u0430\u044E\u0442 \u0434\u0432\u0430 \u0442\u0438\u043F\u0430 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u044F. \u041E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u043F\u043E \u043F\u0440\u0435\u0446\u0435\u0434\u0435\u043D\u0442\u0430\u043C, \u0438\u043B\u0438 \u0438\u043D\u0434\u0443\u043A\u0442\u0438\u0432\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435, \u043E\u0441\u043D\u043E\u0432\u0430\u043D\u043E \u043D\u0430 \u0432\u044B\u044F\u0432\u043B\u0435\u043D\u0438\u0438 \u0437\u0430\u043A\u043E\u043D\u043E\u043C\u0435\u0440\u043D\u043E\u0441\u0442\u0435\u0439 \u0432 \u044D\u043C\u043F\u0438\u0440\u0438\u0447\u0435\u0441\u043A\u0438\u0445 \u0434\u0430\u043D\u043D\u044B\u0445. \u0414\u0435\u0434\u0443\u043A\u0442\u0438\u0432\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u043F\u0440\u0435\u0434\u043F\u043E\u043B\u0430\u0433\u0430\u0435\u0442 \u0444\u043E\u0440\u043C\u0430\u043B\u0438\u0437\u0430\u0446\u0438\u044E \u0437\u043D\u0430\u043D\u0438\u0439 \u044D\u043A\u0441\u043F\u0435\u0440\u0442\u043E\u0432 \u0438 \u0438\u0445 \u043F\u0435\u0440\u0435\u043D\u043E\u0441 \u0432 \u043A\u043E\u043C\u043F\u044C\u044E\u0442\u0435\u0440 \u0432 \u0432\u0438\u0434\u0435 \u0431\u0430\u0437\u044B \u0437\u043D\u0430\u043D\u0438\u0439. \u0414\u0435\u0434\u0443\u043A\u0442\u0438\u0432\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u043F\u0440\u0438\u043D\u044F\u0442\u043E \u043E\u0442\u043D\u043E\u0441\u0438\u0442\u044C \u043A \u043E\u0431\u043B\u0430\u0441\u0442\u0438 \u044D\u043A\u0441\u043F\u0435\u0440\u0442\u043D\u044B\u0445 \u0441\u0438\u0441\u0442\u0435\u043C, \u043F\u043E\u044D\u0442\u043E\u043C\u0443 \u0442\u0435\u0440\u043C\u0438\u043D\u044B \u043C\u0430\u0448\u0438\u043D\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u0438 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u043F\u043E \u043F\u0440\u0435\u0446\u0435\u0434\u0435\u043D\u0442\u0430\u043C \u043C\u043E\u0436\u043D\u043E \u0441\u0447\u0438\u0442\u0430\u0442\u044C \u0441\u0438\u043D\u043E\u043D\u0438\u043C\u0430\u043C\u0438. \u041C\u0430\u0448\u0438\u043D\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u043D\u0430\u0445\u043E\u0434\u0438\u0442\u0441\u044F \u043D\u0430 \u0441\u0442\u044B\u043A\u0435 \u043C\u0430\u0442\u0435\u043C\u0430\u0442\u0438\u0447\u0435\u0441\u043A\u043E\u0439 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043A\u0438, \u043C\u0435\u0442\u043E\u0434\u043E\u0432 \u043E\u043F\u0442\u0438\u043C\u0438\u0437\u0430\u0446\u0438\u0438 \u0438 \u0434\u0438\u0441\u043A\u0440\u0435\u0442\u043D\u043E\u0439 \u043C\u0430\u0442\u0435\u043C\u0430\u0442\u0438\u043A\u0438, \u043D\u043E \u0438\u043C\u0435\u0435\u0442 \u0442\u0430\u043A\u0436\u0435 \u0438 \u0441\u043E\u0431\u0441\u0442\u0432\u0435\u043D\u043D\u0443\u044E \u0441\u043F\u0435\u0446\u0438\u0444\u0438\u043A\u0443, \u0441\u0432\u044F\u0437\u0430\u043D\u043D\u0443\u044E \u0441 \u043F\u0440\u043E\u0431\u043B\u0435\u043C\u0430\u043C\u0438 \u0432\u044B\u0447\u0438\u0441\u043B\u0438\u0442\u0435\u043B\u044C\u043D\u043E\u0439 \u044D\u0444\u0444\u0435\u043A\u0442\u0438\u0432\u043D\u043E\u0441\u0442\u0438 \u0438 \u043F\u0435\u0440\u0435\u043E\u0431\u0443\u0447\u0435\u043D\u0438\u044F. \u041C\u043D\u043E\u0433\u0438\u0435 \u043C\u0435\u0442\u043E\u0434\u044B \u0438\u043D\u0434\u0443\u043A\u0442\u0438\u0432\u043D\u043E\u0433\u043E \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u044F \u0440\u0430\u0437\u0440\u0430\u0431\u0430\u0442\u044B\u0432\u0430\u043B\u0438\u0441\u044C \u043A\u0430\u043A \u0430\u043B\u044C\u0442\u0435\u0440\u043D\u0430\u0442\u0438\u0432\u0430 \u043A\u043B\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043A\u0438\u043C \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043A\u0438\u043C \u043F\u043E\u0434\u0445\u043E\u0434\u0430\u043C. \u041C\u043D\u043E\u0433\u0438\u0435 \u043C\u0435\u0442\u043E\u0434\u044B \u0442\u0435\u0441\u043D\u043E \u0441\u0432\u044F\u0437\u0430\u043D\u044B \u0441 \u0438\u0437\u0432\u043B\u0435\u0447\u0435\u043D\u0438\u0435\u043C \u0438\u043D\u0444\u043E\u0440\u043C\u0430\u0446\u0438\u0438, \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u0443\u0430\u043B\u044C\u043D\u044B\u043C \u0430\u043D\u0430\u043B\u0438\u0437\u043E\u043C \u0434\u0430\u043D\u043D\u044B\u0445 (Data Mining)."@ru ,
"Uczenie maszynowe albo uczenie si\u0119 maszyn, systemy ucz\u0105ce si\u0119 (ang. machine learning) \u2013 stosunkowo m\u0142oda i szybko rozwijaj\u0105ca si\u0119 dziedzina wchodz\u0105ca w sk\u0142ad nauk zajmuj\u0105cych si\u0119 problematyk\u0105 SI. Jest to nauka interdyscyplinarna ze szczeg\u00F3lnym uwzgl\u0119dnieniem takich dziedzin jak informatyka, robotyka i statystyka. G\u0142\u00F3wnym celem jest praktyczne zastosowanie dokona\u0144 w dziedzinie sztucznej inteligencji do stworzenia automatycznego systemu potrafi\u0105cego doskonali\u0107 si\u0119 przy pomocy zgromadzonego do\u015Bwiadczenia i nabywania na tej podstawie nowej wiedzy. Uczenie maszynowe jest konsekwencj\u0105 rozwoju idei sztucznej inteligencji i metod jej wdra\u017Cania praktycznego. Dotyczy rozwoju oprogramowania stosowanego zw\u0142aszcza w innowacyjnych technologiach i przemy\u015Ble. Odpowiednie algorytmy maj\u0105 pozwoli\u0107 oprogramowaniu na zautomatyzowanie procesu pozyskiwania i analizy danych do ulepszania i rozwoju w\u0142asnego systemu. Uczenie si\u0119 mo\u017Ce by\u0107 rozpatrywane jako konkretyzacja algorytmu czyli dob\u00F3r parametr\u00F3w, nazywanych wiedz\u0105 lub umiej\u0119tno\u015Bci\u0105. S\u0142u\u017Cy do tego wiele typ\u00F3w metod pozyskiwania wiedzy oraz sposob\u00F3w reprezentowania wiedzy. Ma to zapewni\u0107 zwi\u0119kszanie: efektywno\u015Bci wydajno\u015Bci bezawaryjno\u015Bci redukcji koszt\u00F3w"@pl ,
"Maskinl\u00E6ring er et fag innen informatikk, n\u00E6rt knyttet til kunstig intelligens. Fagfeltet studerer teknikker som gj\u00F8r maskinen istand til \u00E5 l\u00E6re, alts\u00E5 p\u00E5 egen h\u00E5nd kunne forbedre sin evne til probleml\u00F8sing. Det er ikke tilstrekkelig at maskinen kan operere mot omgivelsene, men den skal ogs\u00E5 kunne endre adferd i noen grad. Dette fordrer da at maskinen vet \u00E5 verdsette de resultat som handlingene gir, at det knyttes verdier som kan bel\u00F8nne gode handlinger og straffe uheldige aksjoner. Maskinens interne l\u00E6re-algoritme m\u00E5 n\u00F8dvendigvis v\u00E6re fastprogrammert, da en ikke her inkluderer de s\u00E5kalt selv-modifiserende program. Algoritmene kan dog byttes ut underveis, grunnet vedlikehold, forbedringer eller vesentlige endringer i problemstilling og milj\u00F8. De viktigste anvendelsene av maskinl\u00E6ring er innen beslutningsst\u00F8ttesystemer, der man skal ekstrahere kortfattede oversikter fra store mengder data (ogs\u00E5 kjent som data mining) stemmegjenkjenning, automatisk f\u00F8ring av kj\u00F8ret\u00F8y, samt andre omr\u00E5der der anledningen til forprogrammering av maskinen er begrenset Alle norske universitet, og enkelte h\u00F8gskoler, tilbyr studier som inkluderer maskinl\u00E6ring som tema."@no ,
"Koneoppiminen on teko\u00E4lyn osa-alue, jossa tutkitaan \u201Doppivien\u201D algoritmien ja tekniikoiden kehitt\u00E4mist\u00E4. Oppimista on kahdenlaista: induktiivista ja deduktiivista. Induktiiviset koneoppimismenetelm\u00E4t muodostavat s\u00E4\u00E4nt\u00F6j\u00E4 ja malleja suurista tietojoukoista. Koneoppimisella on paljon yhteist\u00E4 tilastotieteen kanssa, koska molemmissa tehd\u00E4\u00E4n p\u00E4\u00E4telmi\u00E4 aineistosta, mutta koneoppimisessa selvitet\u00E4\u00E4n ohjelmallisten toteutusten laskennallista vaativuutta. Monet p\u00E4\u00E4ttelyongelmat ovat NP-kovia tai vaikeampia, joten koneoppimistutkimukseen kuuluu my\u00F6s likim\u00E4\u00E4r\u00E4isten p\u00E4\u00E4ttelyalgoritmien kehitt\u00E4minen."@fi ,
"Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn based on data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. Hence, machine learning is closely related to fields such as statistics, probability theory, data mining, pattern recognition, artificial intelligence, adaptive control, and theoretical computer science."@en ,
"Makine \u00F6\u011Frenimi, bilgisayarlar\u0131n alg\u0131lay\u0131c\u0131 verisi ya da veritabanlar\u0131 gibi veri t\u00FCrlerine dayal\u0131 \u00F6\u011Frenimini olanakl\u0131 k\u0131lan algoritmalar\u0131n tasar\u0131m ve geli\u015Ftirme s\u00FCre\u00E7lerini konu edinen bir bilim dal\u0131d\u0131r. Makine \u00F6\u011Frenimi ara\u015Ft\u0131rmalar\u0131n\u0131n odakland\u0131\u011F\u0131 konu bilgisayarlara karma\u015F\u0131k \u00F6r\u00FCnt\u00FCleri alg\u0131lama ve veriye dayal\u0131 ak\u0131lc\u0131 kararlar verebilme becerisi kazand\u0131rmakt\u0131r. Bu, makine \u00F6\u011Freniminin istatistik, olas\u0131l\u0131k kuram\u0131, veri madencili\u011Fi, \u00F6r\u00FCnt\u00FC tan\u0131ma, yapay zeka, uyarlamal\u0131 denetim ve kuramsal bilgisayar bilimi gibi alanlarla yak\u0131ndan ilintili oldu\u011Funu g\u00F6stermektedir."@tr ,
"\u673A\u5668\u5B66\u4E60\uFF0C\u662F\u4EBA\u5DE5\u667A\u80FD\u7684\u4E00\u4E2A\u5B50\u9886\u57DF\uFF0C\u4E3B\u8981\u5173\u6CE8\u65BC\u5F00\u53D1\u4E00\u4E9B\u8BA9\u8BA1\u7B97\u673A\u53EF\u4EE5\u81EA\u52A8\u201C\u5B66\u4E60\u201D\u7684\u6280\u672F\u3002\u66F4\u5177\u4F53\u8BF4\uFF0C\u673A\u5668\u5B66\u4E60\u662F\u4E00\u79CD\u7528\u4E8E\u521B\u5EFA\u6570\u636E\u96C6\u5206\u6790\u5206\u6790\u7A0B\u5E8F\u7684\u65B9\u6CD5\u3002\u673A\u5668\u5B66\u4E60\u8DDF\u7EDF\u8BA1\u5B66\u6709\u7740\u91CD\u8981\u7684\u5173\u7CFB\uFF0C\u56E0\u4E3A\u8FD9\u4E24\u4E2A\u9886\u57DF\u90FD\u662F\u7814\u7A76\u6570\u636E\u5206\u6790\uFF0C\u4F46\u662F\u53C8\u4E0D\u50CF\u7EDF\u8BA1\u5B66\uFF0C\u673A\u5668\u5B66\u4E60\u5173\u6CE8\u7684\u662F\u8BA1\u7B97\u5B9E\u73B0\u7684\u7B97\u6CD5\u590D\u6742\u5EA6\u3002\u5F88\u591A\u63A8\u8BBA\u95EE\u9898\u5C5E\u4E8E\u65E0\u7A0B\u5E8F\u53EF\u5FAA\u96BE\u5EA6\uFF0C\u6240\u4EE5\u90E8\u5206\u7684\u673A\u5668\u5B66\u4E60\u7814\u7A76\u662F\u5F00\u53D1\u5BB9\u6613\u5904\u7406\u7684\u8FD1\u4F3C\u7B97\u6CD5\u3002 \u673A\u5668\u5B66\u4E60\u5DF2\u7ECF\u6709\u4E86\u5341\u5206\u5E7F\u6CDB\u7684\u5E94\u7528\u4F8B\u5982\u751F\u7269\u7279\u5F81\u8BC6\u522B\u3001\u641C\u7D22\u5F15\u64CE\u3001\u533B\u5B66\u8BCA\u65AD\u3001\u68C0\u6D4B\u4FE1\u7528\u5361\u6B3A\u8BC8\u3001\u8BC1\u5238\u5E02\u573A\u5206\u6790\u3001DNA\u5E8F\u5217\u6D4B\u5E8F\u3001\u8BED\u97F3\u548C\u624B\u5199\u8BC6\u522B\u3001\u8BA1\u7B97\u673A\u89C6\u89C9\u3001\u6218\u7565\u6E38\u620F\u548C\u673A\u5668\u4EBA\u8FD0\u7528\u3002"@zh ,
"Maskininl\u00E4rning \u00E4r vad man inom artificiell intelligens brukar kalla statistiska metoder f\u00F6r regression eller klassificering."@sv ,
"\u6A5F\u68B0\u5B66\u7FD2\uFF08\u304D\u304B\u3044\u304C\u304F\u3057\u3085\u3046\u3001Machine learning\uFF09\u3068\u306F\u3001\u4EBA\u5DE5\u77E5\u80FD\u306B\u304A\u3051\u308B\u7814\u7A76\u8AB2\u984C\u306E\u4E00\u3064\u3067\u3001\u4EBA\u9593\u304C\u81EA\u7136\u306B\u884C\u3063\u3066\u3044\u308B\u5B66\u7FD2\u80FD\u529B\u3068\u540C\u69D8\u306E\u6A5F\u80FD\u3092\u30B3\u30F3\u30D4\u30E5\u30FC\u30BF\u3067\u5B9F\u73FE\u3055\u305B\u308B\u305F\u3081\u306E\u6280\u8853\u30FB\u624B\u6CD5\u306E\u3053\u3068\u3067\u3042\u308B\u3002 \u3042\u308B\u7A0B\u5EA6\u306E\u6570\u306E\u30B5\u30F3\u30D7\u30EB\u30C7\u30FC\u30BF\u96C6\u5408\u3092\u5BFE\u8C61\u306B\u89E3\u6790\u3092\u884C\u3044\u3001\u305D\u306E\u30C7\u30FC\u30BF\u304B\u3089\u6709\u7528\u306A\u898F\u5247\u3001\u30EB\u30FC\u30EB\u3001\u77E5\u8B58\u8868\u73FE\u3001\u5224\u65AD\u57FA\u6E96\u306A\u3069\u3092\u62BD\u51FA\u3059\u308B\u3002 \u30C7\u30FC\u30BF\u96C6\u5408\u3092\u89E3\u6790\u3059\u308B\u305F\u3081\u3001\u7D71\u8A08\u5B66\u3068\u306E\u95A2\u9023\u3082\u975E\u5E38\u306B\u6DF1\u3044\u3002 \u6A5F\u68B0\u5B66\u7FD2\u306F\u691C\u7D22\u30A8\u30F3\u30B8\u30F3\u3001\u533B\u7642\u8A3A\u65AD\u3001\u30B9\u30D1\u30E0\u30E1\u30FC\u30EB\u306E\u691C\u51FA\u3001\u91D1\u878D\u5E02\u5834\u306E\u4E88\u6E2C\u3001DNA\u914D\u5217\u306E\u5206\u985E\u3001\u97F3\u58F0\u8A8D\u8B58\u3084\u6587\u5B57\u8A8D\u8B58\u306A\u3069\u306E\u30D1\u30BF\u30FC\u30F3\u8A8D\u8B58\u3001\u30B2\u30FC\u30E0\u6226\u7565\u3001\u30ED\u30DC\u30C3\u30C8\u3001\u306A\u3069\u5E45\u5E83\u3044\u5206\u91CE\u3067\u7528\u3044\u3089\u308C\u3066\u3044\u308B\u3002\u5FDC\u7528\u5206\u91CE\u306E\u7279\u6027\u306B\u5FDC\u3058\u3066\u5B66\u7FD2\u624B\u6CD5\u3082\u9069\u5207\u306B\u9078\u629E\u3059\u308B\u5FC5\u8981\u304C\u3042\u308A\u3001\u69D8\u3005\u306A\u624B\u6CD5\u304C\u63D0\u6848\u3055\u308C\u3066\u3044\u308B\u3002\u305D\u308C\u3089\u306E\u624B\u6CD5\u306F\u3001Machine Learning \u3084 IEEE Transactions on Pattern Analysis and Machine Intelligence \u306A\u3069\u306E\u5B66\u8853\u96D1\u8A8C\u306A\u3069\u3067\u767A\u8868\u3055\u308C\u308B\u3053\u3068\u304C\u591A\u3044\u3002 \u6A5F\u68B0\u5B66\u7FD2\u306E\u30A2\u30EB\u30B4\u30EA\u30BA\u30E0\u306F\u5927\u304D\u304F\u5206\u3051\u3066\u4EE5\u4E0B\u306E\u3088\u3046\u306B\u5206\u985E\u3055\u308C\u308B\u3002 \u6559\u5E2B\u3042\u308A\u5B66\u7FD2 \u6559\u5E2B\u306A\u3057\u5B66\u7FD2 \u5F37\u5316\u5B66\u7FD2"@ja ,
"A aprendizagem de m\u00E1quina \u00E9 um sub-campo da intelig\u00EAncia artificial dedicado ao desenvolvimento de algoritmos e t\u00E9cnicas que permitam ao computador aprender, isto \u00E9, que permitam ao computador aperfei\u00E7oar seu desempenho em alguma tarefa. Em um n\u00EDvel geral, existem dois tipos de aprendizado: indutivo, que extrai regras e padr\u00F5es de grandes conjuntos de dados, e dedutivo. Algumas partes da aprendizagem de m\u00E1quina est\u00E3o intimamente ligadas \u00E0 minera\u00E7\u00E3o de dados e estat\u00EDstica. Sua pesquisa foca nas propriedades dos m\u00E9todos estat\u00EDsticos, assim como sua complexidade computacional. Sua aplica\u00E7\u00E3o pr\u00E1tica inclui o processamento de linguagem natural, motores de busca, diagn\u00F3sticos m\u00E9dicos, bioinform\u00E1tica, reconhecimento de fala, reconhecimento de escrita, vis\u00E3o computacional e locomo\u00E7\u00E3o de rob\u00F4s. Intelig\u00EAncia artifical Bioinform\u00E1tica Vis\u00E3o computacional Minera\u00E7\u00E3o de dados Reconhecimento de padr\u00F5es"@pt ,
"El Aprendizaje Autom\u00E1tico o M\u00E1quinas de Aprendizaje es una rama de la Inteligencia Artificial cuyo objetivo es desarrollar t\u00E9cnicas que permitan a las computadoras aprender. De forma m\u00E1s concreta, se trata de crear programas capaces de generalizar comportamientos a partir de una informaci\u00F3n no estructurada suministrada en forma de ejemplos. Es, por lo tanto, un proceso de inducci\u00F3n del conocimiento. En muchas ocasiones el campo de actuaci\u00F3n del Aprendizaje Autom\u00E1tico se solapa con el de la Estad\u00EDstica, ya que las dos disciplinas se basan en el an\u00E1lisis de datos. Sin embargo, el Aprendizaje Autom\u00E1tico se centra m\u00E1s en el estudio de la Complejidad Computacional de los problemas. Muchos problemas son de clase NP-hard, por lo que gran parte de la investigaci\u00F3n realizada en Aprendizaje Autom\u00E1tico est\u00E1 enfocada al dise\u00F1o de soluciones factibles a esos problemas. El Aprendizaje Autom\u00E1tico puede ser visto como un intento de automatizar algunas partes del M\u00E9todo Cient\u00EDfico mediante m\u00E9todos matem\u00E1ticos. El Aprendizaje Autom\u00E1tico tiene una amplia gama de aplicaciones, incluyendo motores de b\u00FAsqueda, diagn\u00F3sticos m\u00E9dicos, detecci\u00F3n de fraude en el uso de tarjetas de cr\u00E9dito, an\u00E1lisis del mercado de valores, clasificaci\u00F3n de secuencias de ADN, reconocimiento del habla y del lenguaje escrito, juegos y rob\u00F3tica."@es ,
"Strojov\u00E9 u\u010Den\u00ED je podoblast\u00ED um\u011Bl\u00E9 inteligence, zab\u00FDvaj\u00EDc\u00ED se algoritmy a technikami, kter\u00E9 umo\u017E\u0148uj\u00ED po\u010D\u00EDta\u010Dov\u00E9mu syst\u00E9mu 'u\u010Dit se'. U\u010Den\u00EDm v dan\u00E9m kontextu rozum\u00EDme takovou zm\u011Bnu vnit\u0159n\u00EDho stavu syst\u00E9mu, kter\u00E1 zefektivn\u00ED schopnost p\u0159izp\u016Fsoben\u00ED se zm\u011Bn\u00E1m okoln\u00EDho prost\u0159ed\u00ED. Strojov\u00E9 u\u010Den\u00ED se zna\u010Dn\u011B prol\u00EDn\u00E1 s oblastmi statistiky a dob\u00FDvan\u00ED znalost\u00ED a m\u00E1 \u0161irok\u00E9 uplatn\u011Bn\u00ED. Jeho techniky se vyu\u017E\u00EDvaj\u00ED nap\u0159. v biomedic\u00EDnsk\u00E9 informatice, rozli\u0161en\u00ED neleg\u00E1ln\u00EDho u\u017Eit\u00ED kreditn\u00EDch karet, rozpozn\u00E1v\u00E1n\u00ED \u0159e\u010Di a psan\u00E9ho textu, \u010Di mnoh\u00E9 dal\u0161\u00ED."@cs ,
"L'apprendimento automatico (noto in letteratura come Machine Learning) rappresenta una delle aree fondamentali dell'Intelligenza Artificiale e si occupa della realizzazione di sistemi che si basano su osservazioni o esempi come dati per la sintesi di nuova conoscenza (classificazioni, generalizzazioni, riformulazioni). Sono numerose le situazioni di difficile soluzione mediante algoritmi tradizionali. Queste tipicamente sono dovute alla presenza di uno o pi\u00F9 dei seguenti fattori: Difficolt\u00E0 di formalizzazione. Per esempio ognuno di noi sa riconoscere se una certa immagine contenga la faccia di un amico ma probabilmente nessuno sa descrivere una sequenza di passi computazionali che, eseguita sui pixel, consenta di rispondere alla domanda. Elevato numero di variabili in gioco Mancanza di teoria. Per esempio non esistono leggi matematiche note che regolino con esattezza l'andamento dei mercati finanziari. Necessit\u00E0 di personalizzazione. Se per esempio vogliamo classificare documenti come interessanti o non interessanti, la distinzione pu\u00F2 dipendere significativamente dal particolare utente. Gli algoritmi di apprendimento automatico sono tradizionalmente divisi in tre principali tipologie: Apprendimento supervisionato: un istruttore fornisce esempi (e controesempi) di quello che si deve apprendere Apprendimento non supervisionato parte da osservazioni non preclassificate Apprendimento con rinforzo L'analisi dell'apprendimento automatico \u00E8 nota come teoria dell'apprendimento."@it ,
"Maschinelles Lernen ist ein Oberbegriff f\u00FCr die \u201Ek\u00FCnstliche\u201C Generierung von Wissen aus Erfahrung: Ein k\u00FCnstliches System lernt aus Beispielen und kann nach Beendigung der Lernphase verallgemeinern. Das hei\u00DFt, es lernt nicht einfach die Beispiele auswendig, sondern es \u201Eerkennt\u201C Gesetzm\u00E4\u00DFigkeiten in den Lerndaten. So kann das System auch unbekannte Daten beurteilen. Aus dem weiten Spektrum m\u00F6glicher Anwendungen seien hier genannt automatisierte Diagnoseverfahren, Erkennung von Kreditkartenbetrug, Aktienmarktanalysen, Klassifikation von DNA-Sequenzen, Sprach- und Schrifterkennung und autonome Systeme."@de ,
"L'Aprenentatge autom\u00E0tic \u00E9s un camp de la intel\u00B7lig\u00E8ncia artificial que est\u00E0 dedicat al disseny, an\u00E0lisi i desenvolupament d'algoritmes i t\u00E8cniques que permeten que les m\u00E0quines evolucionin. L'aprenentatge autom\u00E0tic est\u00E0 relacionat amb altres camps. T\u00E9 un cert solapament amb l'estad\u00EDstica. En particular, t\u00E9 solapament amb els m\u00E8todes de construcci\u00F3 de models, o l'aprenentatge estad\u00EDstic. Tamb\u00E9 hi ha punts de contacte amb la inform\u00E0tica te\u00F2rica. Aix\u00F2 \u00E9s degut a la complexitat computacional dels problemes. La majoria s\u00F3n de la classe NP-hard. L'aprenentatge s'ha aplicat en molts camps diferents. Podem destacar les aplicacions dedicades al processament del llenguatge natural, als algorismes de cerca, la diagn\u00F2si m\u00E8dica, la bioinform\u00E0tica, la detecci\u00F3 de fraus i la classificaci\u00F3."@ca ,
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\u0437\u0430\u0441\u0442\u043E\u0441\u043E\u0432\u0443\u044E\u0442\u044C\u0441\u044F \u0434\u043B\u044F \u0440\u043E\u0437\u043F\u0456\u0437\u043D\u0430\u0432\u0430\u043D\u043D\u044F \u0442\u0435\u043A\u0441\u0442\u0456\u0432. \u041D\u0430\u0432\u0447\u0430\u043D\u043D\u044F \u0431\u0435\u0437 \u0432\u0447\u0438\u0442\u0435\u043B\u044F \u041D\u0430\u0432\u0447\u0430\u043D\u043D\u044F \u0437 \u0437\u0430\u043A\u0440\u0456\u043F\u043B\u0435\u043D\u043D\u044F\u043C: \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C \u043D\u0430\u0432\u0447\u0430\u0454\u0442\u044C\u0441\u044F \u0437\u0430 \u0434\u043E\u043F\u043E\u043C\u043E\u0433\u043E\u044E \u0442\u0430\u043A\u0442\u0438\u043A\u0438 \u043D\u0430\u0433\u043E\u0440\u043E\u0434\u0438 \u0442\u0430 \u043F\u043E\u043A\u0430\u0440\u0430\u043D\u043D\u044F \u0434\u043B\u044F \u043C\u0430\u043A\u0441\u0438\u043C\u0456\u0437\u0430\u0446\u0456\u0457 \u0432\u0438\u0433\u043E\u0434\u0438 \u0434\u043B\u044F \u0430\u0433\u0435\u043D\u0442\u0456\u0432 (\u0441\u0438\u0441\u0442\u0435\u043C \u0434\u043E \u044F\u043A\u0438\u0445 \u043D\u0430\u043B\u0435\u0436\u0438\u0442\u044C \u043A\u043E\u043C\u043F\u043E\u043D\u0435\u043D\u0442\u0430, \u0449\u043E \u043D\u0430\u0432\u0447\u0430\u0454\u0442\u044C\u0441\u044F)"@uk ,
"L'apprentissage automatique (machine-learning en anglais) est un des champs d'\u00E9tude de l'intelligence artificielle. L'apprentissage automatique fait r\u00E9f\u00E9rence au d\u00E9veloppement, \u00E0 l'analyse et \u00E0 l'impl\u00E9mentation de m\u00E9thodes qui permettent \u00E0 une machine (au sens large) d'\u00E9voluer gr\u00E2ce \u00E0 un processus d'apprentissage, et ainsi de remplir des t\u00E2ches qu'il est difficile ou impossible de remplir par des moyens algorithmiques plus classiques. Voici deux exemples d'applications de l'apprentissage automatique: On peut concevoir un syst\u00E8me d'apprentissage automatique permettant \u00E0 un robot, ayant la capacit\u00E9 de bouger ses membres mais ne sachant rien de la coordination des mouvements permettant la marche, d'apprendre \u00E0 marcher. Le robot commencera par effectuer des mouvements al\u00E9atoires, puis, en privil\u00E9giant les mouvements lui permettant d'avancer, mettra peu \u00E0 peu en place une marche de plus en plus efficace. La reconnaissance de caract\u00E8res est une t\u00E2che complexe car deux caract\u00E8res similaires ne sont jamais exactement \u00E9gaux. On peut concevoir un syst\u00E8me d'apprentissage automatique qui apprend \u00E0 reconna\u00EEtre des caract\u00E8res en observant des exemples, c'est-\u00E0-dire des caract\u00E8res connus. Il est tentant de s'inspirer des \u00EAtres vivants pour concevoir des machines capables d'apprendre. Ainsi, m\u00EAme si l'apprentissage automatique est avant tout un sous-domaine de l'informatique, il est \u00E9galement intimement li\u00E9 aux sciences cognitives, aux neurosciences, \u00E0 la biologie et \u00E0 la psychologie."@fr ,
"Automatisch leren of Machinaal leren is een breed onderzoeksveld binnen kunstmatige intelligentie, en houdt zich bezig met de ontwikkeling van algoritmes en technieken waarmee computers kunnen leren. De methodes zijn te verdelen in twee ruwe categorie\u00EBn: aan-leidinggevend en deductief. Aan-leidinggevende methodes cre\u00EBren computer programma's door het vormen van regels of het extraheren van patronen uit data. Deductieve methoden hebben als resultaat een functie die net zo generiek is als de invoer data. Automatisch leren is sterk gerelateerd aan statistiek, aangezien beide velden de studie van data analyseren. Automatisch leren is echter meer gericht op de algoritmische complexiteit of de implementatie in programma's. Het is ook gerelateerd aan data mining, waarin op een geautomatiseerde manier patronen en relaties worden gezocht in grote hoeveelheden gegevens. Veel leer-problemen zijn NP-hard of moeilijker, dus een belangrijk onderdeel van dit vakgebied is algoritmes te ontwikkelen die de oplossing benaderen."@nl ;
rdfs:comment "Uczenie maszynowe albo uczenie si\u0119 maszyn, systemy ucz\u0105ce si\u0119 (ang. machine learning) \u2013 stosunkowo m\u0142oda i szybko rozwijaj\u0105ca si\u0119 dziedzina wchodz\u0105ca w sk\u0142ad nauk zajmuj\u0105cych si\u0119 problematyk\u0105 SI. Jest to nauka interdyscyplinarna ze szczeg\u00F3lnym uwzgl\u0119dnieniem takich dziedzin jak informatyka, robotyka i statystyka."@pl ,
""@ja ,
"L'apprentissage automatique (machine-learning en anglais) est un des champs d'\u00E9tude de l'intelligence artificielle. L'apprentissage automatique fait r\u00E9f\u00E9rence au d\u00E9veloppement, \u00E0 l'analyse et \u00E0 l'impl\u00E9mentation de m\u00E9thodes qui permettent \u00E0 une machine (au sens large) d'\u00E9voluer gr\u00E2ce \u00E0 un processus d'apprentissage, et ainsi de remplir des t\u00E2ches qu'il est difficile ou impossible de remplir par des moyens algorithmiques plus classiques."@fr ,
"Maskinl\u00E6ring er et fag innen informatikk, n\u00E6rt knyttet til kunstig intelligens. Fagfeltet studerer teknikker som gj\u00F8r maskinen istand til \u00E5 l\u00E6re, alts\u00E5 p\u00E5 egen h\u00E5nd kunne forbedre sin evne til probleml\u00F8sing. Det er ikke tilstrekkelig at maskinen kan operere mot omgivelsene, men den skal ogs\u00E5 kunne endre adferd i noen grad. Dette fordrer da at maskinen vet \u00E5 verdsette de resultat som handlingene gir, at det knyttes verdier som kan bel\u00F8nne gode handlinger og straffe uheldige aksjoner."@no ,
"Makine \u00F6\u011Frenimi, bilgisayarlar\u0131n alg\u0131lay\u0131c\u0131 verisi ya da veritabanlar\u0131 gibi veri t\u00FCrlerine dayal\u0131 \u00F6\u011Frenimini olanakl\u0131 k\u0131lan algoritmalar\u0131n tasar\u0131m ve geli\u015Ftirme s\u00FCre\u00E7lerini konu edinen bir bilim dal\u0131d\u0131r. Makine \u00F6\u011Frenimi ara\u015Ft\u0131rmalar\u0131n\u0131n odakland\u0131\u011F\u0131 konu bilgisayarlara karma\u015F\u0131k \u00F6r\u00FCnt\u00FCleri alg\u0131lama ve veriye dayal\u0131 ak\u0131lc\u0131 kararlar verebilme becerisi kazand\u0131rmakt\u0131r."@tr ,
"\u041C\u0430\u0448\u0438\u0301\u043D\u043D\u0435 \u043D\u0430\u0432\u0447\u0430\u0301\u043D\u043D\u044F \u2014 \u0443\u0437\u0430\u0433\u0430\u043B\u044C\u043D\u0435\u043D\u0430 \u043D\u0430\u0437\u0432\u0430 \u0448\u0442\u0443\u0447\u043D\u043E\u0457 \u0433\u0435\u043D\u0435\u0440\u0430\u0446\u0456\u0457 \u0437\u043D\u0430\u043D\u044C \u0437 \u0434\u043E\u0441\u0432\u0456\u0434\u0443. \u0428\u0442\u0443\u0447\u043D\u0430 \u0441\u0438\u0441\u0442\u0435\u043C\u0430 \u043D\u0430\u0432\u0447\u0430\u0454\u0442\u044C\u0441\u044F \u043D\u0430 \u043F\u0440\u0438\u043A\u043B\u0430\u0434\u0430\u0445 \u0456 \u043F\u0456\u0441\u043B\u044F \u0437\u0430\u043A\u0456\u043D\u0447\u0435\u043D\u043D\u044F \u0444\u0430\u0437\u0438 \u043D\u0430\u0432\u0447\u0430\u043D\u043D\u044F \u043C\u043E\u0436\u0435 \u0443\u0437\u0430\u0433\u0430\u043B\u044C\u043D\u044E\u0432\u0430\u0442\u0438."@uk ,
"Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn based on data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data."@en ,
"Koneoppiminen on teko\u00E4lyn osa-alue, jossa tutkitaan \u201Doppivien\u201D algoritmien ja tekniikoiden kehitt\u00E4mist\u00E4. Oppimista on kahdenlaista: induktiivista ja deduktiivista. Induktiiviset koneoppimismenetelm\u00E4t muodostavat s\u00E4\u00E4nt\u00F6j\u00E4 ja malleja suurista tietojoukoista. Koneoppimisella on paljon yhteist\u00E4 tilastotieteen kanssa, koska molemmissa tehd\u00E4\u00E4n p\u00E4\u00E4telmi\u00E4 aineistosta, mutta koneoppimisessa selvitet\u00E4\u00E4n ohjelmallisten toteutusten laskennallista vaativuutta."@fi ,
"Strojov\u00E9 u\u010Den\u00ED je podoblast\u00ED um\u011Bl\u00E9 inteligence, zab\u00FDvaj\u00EDc\u00ED se algoritmy a technikami, kter\u00E9 umo\u017E\u0148uj\u00ED po\u010D\u00EDta\u010Dov\u00E9mu syst\u00E9mu 'u\u010Dit se'. U\u010Den\u00EDm v dan\u00E9m kontextu rozum\u00EDme takovou zm\u011Bnu vnit\u0159n\u00EDho stavu syst\u00E9mu, kter\u00E1 zefektivn\u00ED schopnost p\u0159izp\u016Fsoben\u00ED se zm\u011Bn\u00E1m okoln\u00EDho prost\u0159ed\u00ED. Strojov\u00E9 u\u010Den\u00ED se zna\u010Dn\u011B prol\u00EDn\u00E1 s oblastmi statistiky a dob\u00FDvan\u00ED znalost\u00ED a m\u00E1 \u0161irok\u00E9 uplatn\u011Bn\u00ED. Jeho techniky se vyu\u017E\u00EDvaj\u00ED nap\u0159."@cs ,
"L'apprendimento automatico (noto in letteratura come Machine Learning) rappresenta una delle aree fondamentali dell'Intelligenza Artificiale e si occupa della realizzazione di sistemi che si basano su osservazioni o esempi come dati per la sintesi di nuova conoscenza (classificazioni, generalizzazioni, riformulazioni). Sono numerose le situazioni di difficile soluzione mediante algoritmi tradizionali."@it ,
"Automatisch leren of Machinaal leren is een breed onderzoeksveld binnen kunstmatige intelligentie, en houdt zich bezig met de ontwikkeling van algoritmes en technieken waarmee computers kunnen leren. De methodes zijn te verdelen in twee ruwe categorie\u00EBn: aan-leidinggevend en deductief. Aan-leidinggevende methodes cre\u00EBren computer programma's door het vormen van regels of het extraheren van patronen uit data."@nl ,
""@zh ,
"El Aprendizaje Autom\u00E1tico o M\u00E1quinas de Aprendizaje es una rama de la Inteligencia Artificial cuyo objetivo es desarrollar t\u00E9cnicas que permitan a las computadoras aprender. De forma m\u00E1s concreta, se trata de crear programas capaces de generalizar comportamientos a partir de una informaci\u00F3n no estructurada suministrada en forma de ejemplos. Es, por lo tanto, un proceso de inducci\u00F3n del conocimiento."@es ,
"L'Aprenentatge autom\u00E0tic \u00E9s un camp de la intel\u00B7lig\u00E8ncia artificial que est\u00E0 dedicat al disseny, an\u00E0lisi i desenvolupament d'algoritmes i t\u00E8cniques que permeten que les m\u00E0quines evolucionin. L'aprenentatge autom\u00E0tic est\u00E0 relacionat amb altres camps. T\u00E9 un cert solapament amb l'estad\u00EDstica. En particular, t\u00E9 solapament amb els m\u00E8todes de construcci\u00F3 de models, o l'aprenentatge estad\u00EDstic. Tamb\u00E9 hi ha punts de contacte amb la inform\u00E0tica te\u00F2rica."@ca ,
"Maskininl\u00E4rning \u00E4r vad man inom artificiell intelligens brukar kalla statistiska metoder f\u00F6r regression eller klassificering."@sv ,
"\u041C\u0430\u0448\u0438\u043D\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u2014 \u043E\u0431\u0448\u0438\u0440\u043D\u044B\u0439 \u043F\u043E\u0434\u0440\u0430\u0437\u0434\u0435\u043B \u0438\u0441\u043A\u0443\u0441\u0441\u0442\u0432\u0435\u043D\u043D\u043E\u0433\u043E \u0438\u043D\u0442\u0435\u043B\u043B\u0435\u043A\u0442\u0430, \u0438\u0437\u0443\u0447\u0430\u044E\u0449\u0438\u0439 \u043C\u0435\u0442\u043E\u0434\u044B \u043F\u043E\u0441\u0442\u0440\u043E\u0435\u043D\u0438\u044F \u0430\u043B\u0433\u043E\u0440\u0438\u0442\u043C\u043E\u0432, \u0441\u043F\u043E\u0441\u043E\u0431\u043D\u044B\u0445 \u043E\u0431\u0443\u0447\u0430\u0442\u044C\u0441\u044F. \u0420\u0430\u0437\u043B\u0438\u0447\u0430\u044E\u0442 \u0434\u0432\u0430 \u0442\u0438\u043F\u0430 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u044F. \u041E\u0431\u0443\u0447\u0435\u043D\u0438\u0435 \u043F\u043E \u043F\u0440\u0435\u0446\u0435\u0434\u0435\u043D\u0442\u0430\u043C, \u0438\u043B\u0438 \u0438\u043D\u0434\u0443\u043A\u0442\u0438\u0432\u043D\u043E\u0435 \u043E\u0431\u0443\u0447\u0435\u043D\u0438\u0435, \u043E\u0441\u043D\u043E\u0432\u0430\u043D\u043E \u043D\u0430 \u0432\u044B\u044F\u0432\u043B\u0435\u043D\u0438\u0438 \u0437\u0430\u043A\u043E\u043D\u043E\u043C\u0435\u0440\u043D\u043E\u0441\u0442\u0435\u0439 \u0432 \u044D\u043C\u043F\u0438\u0440\u0438\u0447\u0435\u0441\u043A\u0438\u0445 \u0434\u0430\u043D\u043D\u044B\u0445."@ru ,
"A aprendizagem de m\u00E1quina \u00E9 um sub-campo da intelig\u00EAncia artificial dedicado ao desenvolvimento de algoritmos e t\u00E9cnicas que permitam ao computador aprender, isto \u00E9, que permitam ao computador aperfei\u00E7oar seu desempenho em alguma tarefa. Em um n\u00EDvel geral, existem dois tipos de aprendizado: indutivo, que extrai regras e padr\u00F5es de grandes conjuntos de dados, e dedutivo. Algumas partes da aprendizagem de m\u00E1quina est\u00E3o intimamente ligadas \u00E0 minera\u00E7\u00E3o de dados e estat\u00EDstica."@pt ,
"Maschinelles Lernen ist ein Oberbegriff f\u00FCr die \u201Ek\u00FCnstliche\u201C Generierung von Wissen aus Erfahrung: Ein k\u00FCnstliches System lernt aus Beispielen und kann nach Beendigung der Lernphase verallgemeinern. Das hei\u00DFt, es lernt nicht einfach die Beispiele auswendig, sondern es \u201Eerkennt\u201C Gesetzm\u00E4\u00DFigkeiten in den Lerndaten. So kann das System auch unbekannte Daten beurteilen."@de .
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dbpprop:discipline dbpedia:Machine_learning .
dbpedia:How_to_Program_an_AI_to_Learn dbpprop:redirect dbpedia:Machine_learning .
dbpedia:Learning_algorithms dbpprop:redirect dbpedia:Machine_learning .
dbpedia:Machine_Learning dbpprop:redirect dbpedia:Machine_learning .