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Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [ 1 ] [ 2 ] [ 3 ] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...
N W E S ♠ ♥ A ♥ Q J 10 ♦ — ♦ ♣ — ♣ — South to lead ♠ 4 ♥ 2 ♦ — ♣ A South needs all three remaining tricks in a notrump contract. South leads the squeeze card, the ♣ A, and West is squeezed in hearts and spades. If West discards the ♥ A, North's ♥ K becomes a winner. If West discards either spade, North's ♠ J becomes a winner. Note the following features of ...
The rules extraction system (RULES) family is a family of inductive learning that includes several covering algorithms. This family is used to build a predictive model based on given observation. It works based on the concept of separate-and-conquer to directly induce rules from a given training set and build its knowledge repository.
The simple squeeze is the most basic form of a squeeze in contract bridge. When declarer plays a winner in one suit (the squeeze card), an opponent is forced to discard a stopper in one of declarer's two threat suits. The simple squeeze takes place against one opponent only and gains one trick only.
In this case, player allocates higher weight to the actions that had a better outcome and choose his strategy relying on these weights. In machine learning, Littlestone applied the earliest form of the multiplicative weights update rule in his famous winnow algorithm, which is similar to Minsky and Papert's earlier perceptron learning algorithm ...
Introduction to Deep Learning. Deep Learning is a machine learning method based on multilayer neural networks. Its core concept can be traced back to the neural computing models of the 1940s. In the 1980s, the proposal of the backpropagation algorithm made the training of multilayer neural networks possible.
Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through ...
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...