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The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered. In machine learning, one aims to construct algorithms that are able to learn to predict a certain target output. To achieve this, the learning algorithm is presented some training examples that demonstrate the intended relation of input and output values. Then the learner is supposed to approximate the correct output, even for examples that have not been shown during training. Without any additional assumptions, this problem cannot be solved exactly since unseen situations might have an arbitrary output value. The kind of necessary assumptions about the nature of the target function are subsumed in the phrase i

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  • Inductive bias
  • Induktiver Bias
  • Bias induttivo
  • 歸納偏向
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  • Unter Induktivem Bias versteht man die Annahmen, die ein Lernalgorithmus machen muss, um aus Trainingsbeispielen oder Beobachtungen verallgemeinern zu können.
  • 當學習器去預測其未遇到過的輸入的結果時,會做一些假設(Mitchell, 1980)。而學習演算法中的歸納偏置則是這些假設的集合。 機器學習試圖去建造一個可以學習的演算法,用來預測某個目標的結果。要達到此目的,要給於學習演算法一些訓練样本,样本說明輸入與輸出之間的預期關係。然后假设學習器在预测中逼近正确的结果,其中包括在訓練中未出現的样本。既然未知状况可以是任意的結果,若沒有其它額外的假設,這任務就無法解決。這種關於目標函數的必要假設就称为歸納偏置(Mitchell, 1980; desJardins and Gordon, 1995)。 一個典型的歸納偏置例子是奧卡姆剃刀,它假設最簡單而又一致的假设是最佳的。這裡的一致是指學習器的假设會對所有樣本產生正確的結果。 歸納偏置比較正式的定義是基於數學上的邏輯。這裡,歸納偏置是一個與訓練样本一起的邏輯式子,其邏輯上會蘊涵學習器所產生的假设。然而在实际应用中,這種嚴謹形式常常無法適用。在有些情况下,学习器的歸納偏置可能只是一個很粗糙的描述(如在人工神經網路中),甚至更加简单。
  • The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered. In machine learning, one aims to construct algorithms that are able to learn to predict a certain target output. To achieve this, the learning algorithm is presented some training examples that demonstrate the intended relation of input and output values. Then the learner is supposed to approximate the correct output, even for examples that have not been shown during training. Without any additional assumptions, this problem cannot be solved exactly since unseen situations might have an arbitrary output value. The kind of necessary assumptions about the nature of the target function are subsumed in the phrase i
  • Nell'apprendimento automatico, il bias induttivo di un algoritmo è l'insieme di assunzioni che il classificatore usa per predire l'output dati gli input che esso non ha ancora incontrato (Mitchell, 1980). Un classico esempio di bias induttivo è il rasoio di Occam. Tale principio assume che l'ipotesi più semplice consistente con l'insieme di addestramento sia da preferire.
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  • The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered. In machine learning, one aims to construct algorithms that are able to learn to predict a certain target output. To achieve this, the learning algorithm is presented some training examples that demonstrate the intended relation of input and output values. Then the learner is supposed to approximate the correct output, even for examples that have not been shown during training. Without any additional assumptions, this problem cannot be solved exactly since unseen situations might have an arbitrary output value. The kind of necessary assumptions about the nature of the target function are subsumed in the phrase inductive bias. A classical example of an inductive bias is Occam's razor, assuming that the simplest consistent hypothesis about the target function is actually the best. Here consistent means that the hypothesis of the learner yields correct outputs for all of the examples that have been given to the algorithm. Approaches to a more formal definition of inductive bias are based on mathematical logic. Here, the inductive bias is a logical formula that, together with the training data, logically entails the hypothesis generated by the learner. Unfortunately, this strict formalism fails in many practical cases, where the inductive bias can only be given as a rough description (e.g. in the case of neural networks), or not at all.
  • Unter Induktivem Bias versteht man die Annahmen, die ein Lernalgorithmus machen muss, um aus Trainingsbeispielen oder Beobachtungen verallgemeinern zu können.
  • Nell'apprendimento automatico, il bias induttivo di un algoritmo è l'insieme di assunzioni che il classificatore usa per predire l'output dati gli input che esso non ha ancora incontrato (Mitchell, 1980). L'apprendimento automatico mira a costruire algoritmi che siano in grado di apprendere una certa funzione obiettivo. A tale scopo, si fornisce all'algoritmo di apprendimento un insieme di addestramento, che contiene esempi della relazione sottesa tra valori di ingresso e di uscita della funzione obiettivo. Il classificatore deve quindi approssimare la funzione obiettivo a partire da tali esempi. Il tipo di assunzioni che il classificatore effettua sulla natura della funzione obiettivo prende il nome di bias induttivo (Mitchell, 1980; desJardins and Gordon, 1995). Un classico esempio di bias induttivo è il rasoio di Occam. Tale principio assume che l'ipotesi più semplice consistente con l'insieme di addestramento sia da preferire.
  • 當學習器去預測其未遇到過的輸入的結果時,會做一些假設(Mitchell, 1980)。而學習演算法中的歸納偏置則是這些假設的集合。 機器學習試圖去建造一個可以學習的演算法,用來預測某個目標的結果。要達到此目的,要給於學習演算法一些訓練样本,样本說明輸入與輸出之間的預期關係。然后假设學習器在预测中逼近正确的结果,其中包括在訓練中未出現的样本。既然未知状况可以是任意的結果,若沒有其它額外的假設,這任務就無法解決。這種關於目標函數的必要假設就称为歸納偏置(Mitchell, 1980; desJardins and Gordon, 1995)。 一個典型的歸納偏置例子是奧卡姆剃刀,它假設最簡單而又一致的假设是最佳的。這裡的一致是指學習器的假设會對所有樣本產生正確的結果。 歸納偏置比較正式的定義是基於數學上的邏輯。這裡,歸納偏置是一個與訓練样本一起的邏輯式子,其邏輯上會蘊涵學習器所產生的假设。然而在实际应用中,這種嚴謹形式常常無法適用。在有些情况下,学习器的歸納偏置可能只是一個很粗糙的描述(如在人工神經網路中),甚至更加简单。
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