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机器学习词典(The Machine Learning Dictionary)2

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C

 

C4.5
C4.5 is a later version of the ID3 decision tree induction algorithm.
C5
C5 is a later version of the ID3 decision tree induction algorithm.
cell body
The cell body is the large open space in the "middle" of the neuron, between the dendrites and the axon, where the cell nucleus lives. A landmark in biological neuron architecture, but not of significance in relation to artificial neural networks (except for those trying to model biological neurons as distinct from using simplified neuron models to solve diverse problems).
clamping
When a neuron in an neural network has its value forcibly set and fixed to some externally provided value, it is said to be clamped. Such a neuron serves as an input unit for the net.
classes in classification tasks
In a classification task in machine learning, the task is to take each instance and assign it to a particular class. For example, in a machine vision application, the task might involve analysing images of objects on a conveyor belt, and classifying them as nuts, bolts, or other components of some object being assembled. In an optical characterrecognition task, the task would involvetaking instances representing images of characters, and classifying according to which character they are. Frequently in examples, for the sake of simplicity if nothing else, just two classes, sometimes called positive and negative, are used.
concept learningsystem (CLS)
A decision tree induction program - a precursor of ID3.
conjunctive expressions
A conjunctive expression is an expression like:
size=large and colour in {red, orange}
that is the conjunction of two or more simple predicates like size=large. The term conjunction refers to the presence of the logicaloperatorand, which joins or conjoins the simple predicates. Occasionally the conjunctive expression might just consist of a single predicate.

See also propositional learning systems and covering algorithm.

connectionism
Connectionism is the neural network approach to solving problems in artificialintelligence - the idea being that connectionists feel that it is appropriate to encode knowledge in the weighted connections between nodes in a neural net. The word "connectionist" is sometimes used with all the heat and force associated with political "isms": "He's a connectionist" can be just as damning, coming from the wrong person, as "He's a communist (or capitalist)". It is also sometimes used as a simple adjective, as in "NetTalk is a connectionist system."
covering algorithm
A covering algorithm, in the context of propositional learning systems, is an algorithm that develops a cover for the set of positive examples - that is, a set of conjunctive expressions that account for all the examples but none of the non-examples.

The algorithm - given a set of examples:

  1. Start with an empty cover.
  2. Select an example.
  3. Find the set of all conjunctive expressions that cover that example.
  4. Select the "best" expression x from that set, according to some criterion (usually "best" is a compromise between generality and compactness and readability).
  5. Add x to the cover.
  6. Go to step 2, unless there are no examples that are not already covered (in which case, stop).

D

 

decision trees
A decision tree is a tree in which each non-leaf node is labelled with an attribute or a question of some sort, and in which the branches at that node correspond to the possible values of the attribute, or answers to the question. For example, if the attribute was shape, then there would be branches below that node for the possible values of shape, say square, round and triangular. Leaf nodes are labelled with a class. Decision trees are used for classifying instances - one starts at the root of the tree, and, takingappropriate branches according to the attribute or question asked about at each branch node, one eventually comes to a leaf node. The label on that leaf node is the class for that instance.
delta rule
The delta rule in error backpropagation learning specifies the update to be made to each weight during backprop learning. Roughly speaking, it states that the change to the weight from node i to node j should be proportional to output of node j and also proportional to the "local gradient" at node j.

The local gradient, for an output node, is the product to the derivative of the squashing function evaluated at the total net input to node j, and the error signal (i.e. the difference between the target output and the actual output). In the case of a hidden node, the local gradient is the product of the derivative the squashing function (as above) and the weighted sum of the local gradients of the nodes to which node j is connected in subsequent layers of the net. Got it?

dendrite
A dendrite is one of the branches on the input end of a biological neuron. It has connections, via synapses to the axons of other neurons.


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