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An inductive bias allows a learning algorithm to prioritize one solution (or interpretation) over another, independently of the observed data. [3] In machine learning, the aim is 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 ...
Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
Inductive bias occurs within the field of machine learning. In machine learning one seeks to develop algorithms that are able to learn to anticipate a particular output. To accomplish this, the learning algorithm is given training cases that show the expected connection. Then the learner is tested with new examples.
In 2016, the World Economic Forum claimed we are experiencing the fourth wave of the Industrial Revolution: automation using cyber-physical systems. Key elements of this wave include machine ...
Inductive bias, the set of assumptions that a machine learner uses to predict outputs of given inputs that it has not encountered. Weight and bias, two terms used to describe parameters in a neural network. Seat bias, any bias in a method of apportionment that favors either large or small parties over the other
Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning. In supervised learning, an algorithm is given samples that are labeled in some useful way. For example, the samples might be descriptions of mushrooms, and the labels could be whether or not the mushrooms are edible.
Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better. [3]
In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan [1] used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm , and is typically used in the machine learning and natural language processing domains.