An empirical comparison of min–max-modular k-NN with different voting methods to large-scale text categorization

No abstract is available for this paper.

• Publication year

  2008

• Venue

  Soft Computing - A Fusion of Foundations, Methodologies and Applications

• Publication date

  2008-05-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1007/s00500-007-0242-3
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Prediction of Protein Subcellular Multi-locations with a Min-Max Modular Support Vector Machine

  2006cited by this paper
• A Modular Reduction Method for k-NN Algorithm with Self-recombination Learning

  2006cited by this paper
• An experimental study on large-scale web categorization

  2005cited by this paper
• Task Decomposition Using Geometric Relation for Min-Max Modular SVMs

  2005cited by this paper
• Multi-view Face Recognition with Min-Max Modular SVMs

  2005cited by this paper
• Gender Recognition Using a Min-Max Modular Support Vector Machine

  2005cited by this paper
• Fast text categorization with min-max modular support vector machines

  2005cited by this paper
• A Modular k-Nearest Neighbor Classification Method for Massively Parallel Text Categorization

  2004cited by this paper
• Massively parallel classification of single-trial EEG signals using a min-max Modular neural network

  2004cited by this paper
• A part-versus-part method for massively parallel training of support vector machines

  2004cited by this paper
• RCV1: A New Benchmark Collection for Text Categorization Research

  2004cited by this paper
• Machine learning in automated text categorization

  2001cited by this paper
• Text Categorization and Prototypes

  2001cited by this paper
• A re-examination of text categorization methods

  1999cited by this paper
• Text Categorization with Support Vector Machines: Learning with Many Relevant Features

  1999cited by this paper
• Task decomposition and module combination based on class relations: a modular neural network for pattern classification

  1999cited by this paper
• Using Maximum Entropy for Text Classification

  1999cited by this paper
• An Evaluation of Statistical Approaches to Text Categorization

  1999cited by this paper
• Task Decomposition Based on Class Relations: A Modular Neural Network Architecture for Pattern Classification

  1997cited by this paper
• Biological and Artificial Computation: From Neuroscience to Technology

  1997cited by this paper
• A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

  1997cited by this paper
• An example-based mapping method for text categorization and retrieval

  1994cited by this paper
• Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties

  1989influential reference
• The Distance-Weighted k-Nearest-Neighbor Rule

  1976cited by this paper
• Nearest neighbor pattern classification

  1967cited by this paper

• Techniques for text classification: Literature review and current trends

  2015cites this paper
• Comparison of Voting Methods Using Complexity Functions and the Weak Theta Asymptotic Notation

  2015cites this paper
• A Voting Approach for Comparing Several Swarm Intelligence Algorithms

  2015cites this paper
• Determination of E-Commerce Sites by Text Mining

  2014cites this paper
• A Meta-Top-Down Method for Large-Scale Hierarchical Classification

  2014cites this paper
• Metin Madenciliği İle E-Ticaret Sitelerinin Belirlenmesi

  2014cites this paper
• Integrating feature selection and Min-Max Modular SVM for powerful ensemble

  2012cites this paper
• Bisecting data partitioning methods for Min-Max Modular Support Vector Machine

  2011cites this paper
• A Parallel and Modular Pattern Classification Framework for Large-Scale Problems

  2009cites this paper
• Áòòóöôóööøøòò Ôööóö Òóûððððð Òøó Ðððöòòòò Ý Úúúúòò Øöööòòòò Øø ¾ Ååò¹ññü Ñóóùððö Òòøûóöö

  2009cites this paper
• Incorporating prior knowledge into learning by dividing training data

  2009cites this paper
• Comparative Study of the Performance of Ensemble and Base Classifiers in Text Data Categorization

  year unknowncites this paper