Classification method and apparatus based on boosting and pruning of multiple classifiers
September 1, 2019. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.Patent ReferencesNeural network model in pattern recognition using probabilistic contextual information System for self-organization of stable category recognition codes for analog input patterns Neural network apparatus and method for pattern recognition Method for operating an optimal weight pruning apparatus for designing artificial neural networks Method and system for training a neural network with adaptive weight updating and adaptive pruning in principal component space Apparatus and methods for machine learning hypotheses Neural network/conceptual clustering fraud detection architecture Classifying system having a single neural network architecture for multiple input representations Computer input stylus method and apparatus Method for improvement accuracy of decision tree based text categorization Patent #: 6253169 InventorsAssigneeApplicationNo. 388858 filed on 09/01/1999US Classes:706/20, Classification or recognition704/9, Natural language706/16, Learning task706/25Learning methodExaminersPrimary: Starks, Wilbert L. Jr.Attorney, Agent or FirmInternational ClassG06N 003/02AbstractA boosting and pruning system and method for utilizing a plurality of neural networks, preferably those based on adaptive resonance theory (ART), in order to increase pattern classification accuracy is presented. The method utilizes a plurality of N randomly ordered copies of the input data, which is passed to a plurality of sets of booster networks. Each of the plurality of N randomly ordered copies of the input data is divided into a plurality of portions, preferably with an equal allocation of the data corresponding to each class for which recognition is desired. The plurality of portions is used to train the set of booster networks. The rules generated by the set of booster networks are then pruned in an intra-booster pruning step, which uses a pair-wise Fuzzy AND operation to determine rule overlap and to eliminate rules which are sufficiently similar. This process results in a set of intra-booster pruned booster networks. A similar pruning process is applied in an inter-booster pruning process, which eliminates rules from the intra-booster pruned networks with sufficient overlap. The final, derivative booster network captures the essence of the plurality of sets of booster networks and provides for higher classification accuracy than available using a single network.Other References
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