7 research outputs found
Support Vector Machines and Kernel Functions for Text Processing
This work presents kernel functions that can be used in conjunction with the Support Vector Machine â SVM â learning algorithm to solve the automatic text classification task. Initially the Vector Space Model for text processing is presented. According to this model text is seen as a set of vectors in a high dimensional space; then extensions and alternative models are derived, and some preprocessing procedures are discussed. The SVM learning algorithm, largely employed for text classification, is outlined: its decision procedure is obtained as a solution of an optimization problem. The âkernel trickâ, that allows the algorithm to be applied in non-linearly separable cases, is presented, as well as some kernel functions that are currently used in text applications. Finally some text classification experiments employing the SVM classifier are conducted, in order to illustrate some text preprocessing techniques and the presented kernel functions
Contribuição ao estudo e desenvolvimento de um sistema de regras de produção
Dissertação (mestrado) - Universidade Federal de Santa Catarina. Centro TecnologicoA presente dissertação apresenta o SP1, um Sistema de Produção que manipula regras com variĂĄveis quantificadas. O SP1 incorpora uma linguagem para representação do conhecimento, um interpretador e um ambiente para execução. A linguagem SP1 utiliza uma notação predicativa e se caracteriza por sua legibilidade e extensibilidade. Para remediar a ineficĂĄcia da operação de filtragem, crĂtica em um sistema de produção, o interpretador do SP1 incorpora um novo algoritmo baseado na compilação das regras, que procura reduzir ao mĂĄximo os teste a efetuar para a instanciação das regras pela construção de uma rede otimizada. VĂĄrios tipos de redundâncias estruturais sĂŁo assim evitadas. Esta rede ĂŠ utilizada para propagar as modificaçþes produzidas nos dados e atualizar as instâncias sobre as regras. O ambiente de execução inclui as funçþes necessidades para a execução, monitoração e depuração
Support Vector Machines and Kernel Functions for Text Processing
This work presents kernel functions that can be used in conjunction with the Support Vector Machine â SVM â learning algorithm to solve the automatic text classification task. Initially the Vector Space Model for text processing is presented. According to this model text is seen as a set of vectors in a high dimensional space; then extensions and alternative models are derived, and some preprocessing procedures are discussed. The SVM learning algorithm, largely employed for text classification, is outlined: its decision procedure is obtained as a solution of an optimization problem. The âkernel trickâ, that allows the algorithm to be applied in non-linearly separable cases, is presented, as well as some kernel functions that are currently used in text applications. Finally some text classification experiments employing the SVM classifier are conducted, in order to illustrate some text preprocessing techniques and the presented kernel functions