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Chapter and Conference Paper
LRLW-LSI: An Improved Latent Semantic Indexing (LSI) Text Classifier
The task of Text Classification (TC) is to automatically assign natural language texts with thematic categories from a predefined category set. And Latent Semantic Indexing (LSI) is a well known technique in I...
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Chapter and Conference Paper
A New Multi-level Algorithm Based on Particle Swarm Optimization for Bisecting Graph
An important application of graph partitioning is data clustering using a graph model — the pairwise similarities between all data objects form a weighted graph adjacency matrix that contains all necessary inf...
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Chapter and Conference Paper
An Effective Multi-level Algorithm Based on Ant Colony Optimization for Bisecting Graph
An important application of graph partitioning is data clustering using a graph model — the pairwise similarities between all data objects form a weighted graph adjacency matrix that contains all necessary inf...
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Chapter and Conference Paper
Support Vector Regression for Financial Time Series Forecasting
Recently, Support Vector Regression (SVR) has been a popular tool in financial time series forecasting. This study deals with the application of Support Vector Regression in stock composite index forecasting. ...
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Chapter and Conference Paper
An Effective Refinement Algorithm Based on Multilevel Paradigm for Graph Bipartitioning
The min-cut bipartitioning problem is a fundamental partitioning problem and is NP-Complete. It is also NP-Hard to find good approximate solutions for this problem. In this paper, we present a new effective re...
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Chapter and Conference Paper
Parallel Sequence Alignment Algorithm for Clustering System
Sequence alignment is one of the most important fundamental operations in bioinformatics. It has been successfully applied to predict the function, structure and evolution of biological sequences. In this pape...
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Chapter and Conference Paper
An Effective Multi-level Algorithm for Bisecting Graph
Clustering is an important approach to graph partitioning. In this process a graph model expressed as the pairwise similarities between all data objects is represented as a weighted graph adjacency matrix. The...
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Chapter and Conference Paper
A Chaotic Neural Network for the Maximum Clique Problem
This paper applies a chaotic neural network (CNN) to solve the maximum clique problem (MCP), a classic NP-hard and computationally intractable graph optimization problem, which has many real-world applications...
