A New Text Categorization Technique Using Distributional Clustering and Learning Logic

Al-Mubaid, Hisham; Umair, Sajid

doi:10.1109/tkde.2006.135

Cited by 91 publications

(48 citation statements)

References 15 publications

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“…Al-Mubaid and Umair [9] used distributional clustering to generate an efficient representation of documents and applied a learning logic approach for training text classifiers. The Agglomerative Information Bottleneck approach was proposed by Tishby et al [10], [11].…”

Section: Motivation Of the Algorithmmentioning

confidence: 99%

Fast Fuzzy Feature Clustering for Text Classification

Dawar¹

2012

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011. The word in the feature vector of the document is grouped into the cluster in less iteration. The numbers of iterations required to obtain cluster centers are reduced by transforming clusters center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit change is used for dimension reduction. Experimental results show that, this method improve the performance by significantly reducing the number of iterations required to obtain the cluster center. The same is being verified with three benchmark datasets.

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Section: Motivation Of the Algorithmmentioning

confidence: 99%

Fast Fuzzy Feature Clustering for Text Classification

Dawar¹

2012

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…The results of the experiments are contradictory revealing a sensitivity of the algorithm to the datasets. (Mubaid & Umair, 2006) use the IB clustering method with a least squares (Felici & Truemper, 2002) classifier. The method has been tested with the WebKB, 20NG and Reuters-21578 datasets and is compared against SVM.…”

Section: One-way Clustering (Clustering Features)mentioning

confidence: 99%

“…Authors Clustering method (Baker & McCallum, 1998) Distributional clustering ) IB (Verbeerk, 2000a(Verbeerk, , 2000b Agglomerative IB (Bekkerman et al, 2001(Bekkerman et al, , 2003 Agglomerative IB (Mubaid & Umair, 2006) IB One-way clustering: cluster feature space and replace it with a feature cluster representation (Dhillon et al, 2003a) Divisive clustering (Yaniv & Souroujon, 2001) Iterative double clustering (Dhillon et al, 2002(Dhillon et al, , 2003b Information-theoretic coclustering (Dai et al, 2007) Co-clustering classification Co-clustering: cluster both features and documents (Takamura & Matsumoto, 2002); (Takamura, 2003) Two-dimensional clustering Table 1. Clustering as a feature compression and/or extraction method (Fung and Mangasarian, 2001) propose a model for classifying two-class unlabelled data, called clustered concave semi-supervised SVM (CVS 3 VM).…”

Section: Goalmentioning

confidence: 99%

Text Classification Aided by Clustering: a Literature Review

Kyriakopoulou¹

2008

Tools in Artificial Intelligence

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“…Various machine learning software such as Weka, Java Neural Network Framework Neuroph, Scikit Learn, Open NN Multiple Back propagation exists that assists researchers in solving complex problems. These packages have conventional algorithms [1][2][3][4][5][6][7][8][9] for image analysis, machine learning and data mining that assume training and test data have the same distribution. In many real-world applications, this may not hold, for example, if one has to detect users current location using previously collected Wi-Fi data.…”

Section: Introductionmentioning

confidence: 99%

A novel training algorithm for convolutional neural network

Anuse¹,

Vyas²

2016

Complex Intell. Syst.

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Many machine learning softwares are available which help the researchers to accomplish various tasks. These software packages have various conventional algorithms which perform well if the training and test data are independent and identically distributed. However, this might not be the case in the real world. The training data may not be available at one time. In the case of neural networks, the architecture has to be retrained with new data that are made available subsequently. In this paper, we present a novel training algorithm which can avoid complete retraining of any neural network architecture meant for visual pattern recognition. To show the utility of the algorithm, we have investigated the performance of convolutional neural network (CNN) architecture for a face recognition task under transfer learning. The proposed training algorithm may be used for enhancing the utility of machine learning software by providing researchers with an approach that can reduce the training time under transfer learning.

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A New Text Categorization Technique Using Distributional Clustering and Learning Logic

Cited by 91 publications

References 15 publications

Fast Fuzzy Feature Clustering for Text Classification

Fast Fuzzy Feature Clustering for Text Classification

Text Classification Aided by Clustering: a Literature Review

A novel training algorithm for convolutional neural network

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