Inductive bias: Algorithm, Machine learning, Occam's razor, Logic, Neural network, Naive Bayes classifier, Support vector machine, Feature space - Tapa blanda

Sinopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. The inductive bias of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs that it has not encountered (Mitchell, 1980). 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 task 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 term inductive bias (Mitchell, 1980; desJardins and Gordon, 1995).

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