scholarly journals Text Document Classification basedon Least Square Support Vector Machines with Singular Value Decomposition

2011 ◽  
Vol 27 (7) ◽  
pp. 21-26 ◽  
Author(s):  
M.Ramakrishna Murty ◽  
J.V.R Murthy ◽  
Prasad Reddy P.V.G.D
Keyword(s):  
Support Vector Machines ◽  
Singular Value Decomposition ◽  
Document Classification ◽  
Singular Value ◽  
Least Square ◽  
Support Vector ◽  
Text Document ◽  
Vector Machines ◽  
Text Document Classification ◽  
Value Decomposition

Text Document Classification Using Support Vector Machine with Feature Selection Using Singular Value Decomposition

2014 ◽  
Vol 905 ◽  
pp. 528-532
Author(s):  
Hoan Manh Dau ◽  
Ning Xu
Keyword(s):  
Support Vector Machine ◽  
Singular Value Decomposition ◽  
Decision Tree ◽  
Document Classification ◽  
Singular Value ◽  
Multidimensional Data ◽  
Support Vector ◽  
Text Document ◽  
Text Document Classification ◽  
Value Decomposition

Text document classification is content analysis task of the text document and then giving decision (or giving a prediction) whether this text document belongs to which group among given text document ones. There are many classification techniques such as decision method basing on Naive Bayer, decision tree, k-Nearest neighbor (KNN), neural network, Support Vector Machine (SVM) method. Among those techniques, SVM is considered the popular and powerful one, especially, it is suitable to huge and multidimensional data classification. Text document classification with characteristics of very huge dimensional numbers and selecting features before classifying impact the classification results. Support Vector Machine is a very effective method in this field. This article studies Support Vector Machine and applies it in the problem of text document classification. The study shows that Support Vector Machine method with choosing features by singular value decomposition (SVD) method is better than other methods and decision tree.

Digital Watermarking Techniques for Images

2014 ◽  
pp. 737-746
Author(s):  
Channapragada R. S. G. Rao ◽  
Vadlamani Ravi ◽  
Munaga. V. N. K. Prasad ◽  
E. V. Gopal
Keyword(s):  
Support Vector Machines ◽  
Singular Value Decomposition ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Singular Value ◽  
Independent Component ◽  
Support Vector ◽  
Vector Machines ◽  
Value Decomposition ◽  
Cryptographic Techniques

This Chapter presents a brief review of the work done during 1990-2013, in the application of intelligent techniques and independent component analysis to digital image watermarking. The review considers only the gray-scale and color images excluding other multimedia. The intelligent techniques considered are support vector machines, singular value decomposition and cryptographic techniques. The review is structured by considering the type of technique applied to solve the problem as an important dimension. Consequently the papers are grouped into the following four families, (i) Support vector machines, (ii) Singular value decomposition and (iii) Cryptographic Techniques (iv) Independent component analysis. Comparative analysis of different techniques is also presented. Finally, the review is concluded with future directions.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

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