scholarly journals Kernel Methods for Exploratory Pattern Analysis: A Demonstration on Text Data

2004 ◽  
pp. 16-29 ◽  
Author(s):  
Tijl De Bie ◽  
Nello Cristianini
Keyword(s):  
Kernel Methods ◽  
Pattern Analysis ◽  
Text Data ◽  
Exploratory Pattern

A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part II

2012 ◽  
pp. 51-71
Author(s):  
Veena T. ◽  
Dileep A. D. ◽  
C. Chandra Sekhar
Keyword(s):  
Speech Processing ◽  
Kernel Methods ◽  
Text Analysis ◽  
Character Recognition ◽  
Pattern Analysis ◽  
Biological Sequence ◽  
Biological Sequence Analysis ◽  
Handwritten Character ◽  
Varying Length ◽  
Classification And Clustering

Pattern analysis tasks on sequences of discrete symbols are important for pattern discovery in bioinformatics, text analysis, speech processing, and handwritten character recognition. Discrete symbols may correspond to amino acids or nucleotides in biological sequence analysis, characters in text analysis, and codebook indices in processing of speech and handwritten character data. The main issues in kernel methods based approaches to pattern analysis tasks on discrete symbol sequences are related to defining a measure of similarity between sequences of discrete symbols, and handling the varying length nature of sequences. We present a review of methods to design dynamic kernels for sequences of discrete symbols. We then present a review of approaches to classification and clustering of sequences of discrete symbols using the dynamic kernel based methods.

An overview of mapping techniques for exploratory pattern analysis

1988 ◽  
Vol 21 (5) ◽  
pp. 411-429 ◽  
Author(s):  
Wojciech Siedlecki ◽  
Kinga Siedlecka ◽  
Jack Sklansky
Keyword(s):  
Pattern Analysis ◽  
Mapping Techniques ◽  
Exploratory Pattern

A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part I

2012 ◽  
pp. 24-50
Author(s):  
Dileep A. D. ◽  
Veena T. ◽  
C. Chandra Sekhar
Keyword(s):  
Pattern Classification ◽  
Kernel Methods ◽  
Pattern Analysis ◽  
Kernel Functions ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Sequential Data ◽  
Feature Vectors ◽  
Varying Length ◽  
Classification And Clustering

Sequential data mining involves analysis of sequential patterns of varying length. Sequential pattern analysis is important for pattern discovery from sequences of discrete symbols as in bioinformatics and text analysis, and from sequences or sets of continuous valued feature vectors as in processing of audio, speech, music, image, and video data. Pattern analysis techniques using kernel methods have been explored for static patterns as well as sequential patterns. The main issue in sequential pattern analysis using kernel methods is the design of a suitable kernel for sequential patterns of varying length. Kernel functions designed for sequential patterns are known as dynamic kernels. In this chapter, we present a brief description of kernel methods for pattern classification and clustering. Then we describe dynamic kernels for sequences of continuous feature vectors. We then present a review of approaches to sequential pattern classification and clustering using dynamic kernels.

Experiments on mapping techniques for exploratory pattern analysis

1988 ◽  
Vol 21 (5) ◽  
pp. 431-438 ◽  
Author(s):  
Wojciech Siedlecki ◽  
Kinga Siedlecka ◽  
Jack Sklansky
Keyword(s):  
Pattern Analysis ◽  
Mapping Techniques ◽  
Exploratory Pattern

scholarly journals New Mixed Kernel Functions of SVM Used in Pattern Recognition

2016 ◽  
Vol 16 (5) ◽  
pp. 5-14 ◽  
Author(s):  
Hao Huanrui
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Kernel Methods ◽  
Pattern Analysis ◽  
New Technology ◽  
Wavelet Function ◽  
Kernel Functions ◽  
Good Time ◽  
Polynomial Kernel ◽  
Support Vector

Abstract The pattern analysis technology based on kernel methods is a new technology, which combines good performance and strict theory. With support vector machine, pattern analysis is easy and fast. But the existing kernel function fits the requirement. In the paper, we explore the new mixed kernel functions which are mixed with Gaussian and Wavelet function, Gaussian and Polynomial kernel function. With the new mixed kernel functions, we check different parameters. The results shows that the new mixed kernel functions have good time efficiency and accuracy. In image recognition we used SVM with two mixed kernel functions, the mixed kernel function of Gaussian and Wavelet function are suitable for more states.

A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part I

Data Mining ◽  
2013 ◽  
pp. 251-278
Author(s):  
A. D. Dileep ◽  
T. Veena ◽  
C. Chandra Sekhar
Keyword(s):  
Pattern Classification ◽  
Kernel Methods ◽  
Pattern Analysis ◽  
Kernel Functions ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Sequential Data ◽  
Feature Vectors ◽  
Varying Length ◽  
Classification And Clustering

Sequential data mining involves analysis of sequential patterns of varying length. Sequential pattern analysis is important for pattern discovery from sequences of discrete symbols as in bioinformatics and text analysis, and from sequences or sets of continuous valued feature vectors as in processing of audio, speech, music, image, and video data. Pattern analysis techniques using kernel methods have been explored for static patterns as well as sequential patterns. The main issue in sequential pattern analysis using kernel methods is the design of a suitable kernel for sequential patterns of varying length. Kernel functions designed for sequential patterns are known as dynamic kernels. In this chapter, we present a brief description of kernel methods for pattern classification and clustering. Then we describe dynamic kernels for sequences of continuous feature vectors. We then present a review of approaches to sequential pattern classification and clustering using dynamic kernels.

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