Pruning networks
An alternative approach to growing networks is to start with a relatively large network and then remove weights so as to arrive at an optimal network architecture. The usual procedure is as follows: ...
Alpha-Beta Pruning
One of the most elegant of all AI search algorithms is alpha-beta pruning.
Alpha-beta pruning is a search algorithm which seeks to reduce the number of nodes that are evaluated by the minimax algorithm in its search tree.
Pruning Unnecessary Conditions
If there are conditions of that rule that are inconsequential to the outcome, discard them thus simplifying the rule (and thus improving efficiency).
Pruning Hidden Layer Nodes
Another method to improve results is to prune away excess hidden layer units. Unfortunately there are a lot of them and I've never tried to program any of them.
If you have any questions or comments, write me.
Pruning and Selecting the "Right-Sized" Tree
The size of a tree in the classification and regression trees analysis is an important issue, since an unreasonably big tree can only make the interpretation of results more difficult.
pruning decision trees The data used to generate a decision tree, using an algorithm like the ID3 tree induction algorithm, can include noise - that is, instances that contain errors, either in the form of a wrong classification, ...
Tree Pruning
When a decision tree is built, many of the branches will reflect anomalies in the training data due to noise or outliers. Tree pruning methods address this problem of overfitting the data. Scalability and Decision Tree Induction ...
pruning is introduced
rule derivation
Many of these techniques appear in the CART algorithm plus some others so we will go through this introduction in the CART algorithm.
Alpha-beta pruning can be explained simply as a technique for not exploring those branches of a search tree that analysis indicates not to be of further interest either to the player making the analysis (this is obvious) or to his opponent (and this ...
Alpha-beta pruning eliminates parts of the tree that are poor path choices relative to other pathways.
Use of alpha-beta pruning combined with a number of search heuristics dramatically improved the performance of brute-force search algorithms.
Software for searching the move tree (minimax with alphabeta pruning (Knuth and Moore 1975)) was written, ...
Alpha-beta pruning is a search algorithm that reduces the number of nodes that need to be evaluated in the search tree by the minimax algorithm. ... Look up Premise in Wiktionary, the free dictionary. ...
3 Alpha-Beta Pruning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
5.4 Imperfect Real-Time Decisions . . . . . . . . . . . . . . . . . . . . . . . 171
5.5 Stochastic Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The efficiency of the method depends critically on the effectiveness of the branching and bounding algorithms used; bad choices could lead to repeated branching, without any pruning, until the sub-regions become very small.
across the prefrontal and parietal layers (long-distance synaptic strengthening)
3) increased specificity in the tuning curves of each neuron (a combined pattern of synaptic strengthening and synaptic pruning at the local level)
4) increased ...
The adjustment of the complexity of a Decision Tree is often made a posteriori by a so-called pruning mechanism. The Tree is first purposely grown up to an exagerated depth. Then branches that are deemed superfluous are removed.
Histograms ...
The best-developed technology of Heuristic Search is that of game-playing using tree-pruning, plausible-move generation, and terminal-evaluation methods.
 See also: Neural network, Classification, Regression, Data mining, Generalization
  
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