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The intermediate (thin) rectangles represent the hypotheses in the version space. Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction [1]
In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward function) associated with the Markov decision process (MDP), [1] which, in RL, represents the problem to be solved. The transition probability distribution (or transition model) and the reward ...
(square, triangle): the universe contains a square on day 1, and a triangle on day 2 (square, square) (triangle, triangle) (triangle, square) Any prediction strategy that succeeds for history #2, by predicting a square on day 2 if there is a square on day 1, will fail on history #1, and vice versa.
Wolpert had previously derived no free lunch theorems for machine learning (statistical inference). [3] Before Wolpert's article was published, Cullen Schaffer independently proved a restricted version of one of Wolpert's theorems and used it to critique the current state of machine learning research on the problem of induction. [4]
The RNNsearch model introduced an attention mechanism to seq2seq for machine translation to solve the bottleneck problem (of the fixed-size output vector), allowing the model to process long-distance dependencies more easily. The name is because it "emulates searching through a source sentence during decoding a translation".
In machine learning, a key challenge is enabling models to accurately predict outcomes on unseen data, not just on familiar training data. Regularization is crucial for addressing overfitting—where a model memorizes training data details but can't generalize to new data. The goal of regularization is to encourage models to learn the broader ...
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In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant . [ 1 ]