Kernel based algorithms for mining huge data sets: supervised, semi-supervised, and unsupervised learning
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2006
|
| Series: | Studies in computational intelligence
17 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Literaturverz. S. 247 - 255 |
| Physical Description: | XVI, 260 S. Ill., zahlr. graph. Darst. 24 cm |
| ISBN: | 9783540316817 3540316817 |
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MARC
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| 245 | 1 | 0 | |a Kernel based algorithms for mining huge data sets |b supervised, semi-supervised, and unsupervised learning |c Te-Ming Huang ; Vojislav Kecman ; Ivica Kopriva |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2006 | |
| 300 | |a XVI, 260 S. |b Ill., zahlr. graph. Darst. |c 24 cm | ||
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| 490 | 1 | |a Studies in computational intelligence |v 17 | |
| 500 | |a Literaturverz. S. 247 - 255 | ||
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| 650 | 4 | |a Kernel functions | |
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Record in the Search Index
| _version_ | 1804136526947287040 |
|---|---|
| adam_text | Contents
1
Introduction
............................................... 1
1.1 An
Overview of Machine Learning
......................... 1
1.2
Challenges in Machine Learning
........................... 3
1.2.1
Solving Large-Scale SVMs
.......................... 4
1.2.2
Feature Reduction with Support Vector Machines
..... 5
1.2.3
Graph-Based Semi-supervised Learning Algorithms
....
С
1.2.4
Unsupervised Learning Based on Principle
of Redundancy Reduction
.......................... 7
2
Support Vector Machines in Classification
and Regression
—
An Introduction
......................... 11
2.1
Basics of Learning from Data
............................. 12
2.2
Support Vector Machines in Classification and Regression
.... 21
2.2.1
Linear Maximal Margin Classifier
for Linearly Separable Data
......................... 21
2.2.2
Linear Soft Margin Classifier for Overlapping Classes
.. 32
2.2.3
The Nonlinear SVMs Classifier
...................... 36
2.2.4
Regression by Support Vector Machines
.............. 48
2.3
Implementation Issues
................................... 57
3
Iterative Single Data Algorithm for Kernel Machines
from Huge Data Sets: Theory and Performance
............ 61
3.1
Introduction
............................................ 61
3.2
Iterative Single Data Algorithm for Positive Definite Kernels
without Bias Term
6..................................... 63
3.2.1
Kernel AdaTron in Classification
.................... 64
3.2.2
SMO
without Bias Term
b
in Classification
........... 65
3.2.3
Kernel AdaTron in Regression
...................... 66
3.2.4
SMO
without Bias Term
b
in Regression
............. 67
3.2.5
The Coordinate Ascent Based Learning for Nonlinear
Classification and Regression Tasks
.................. 68
XIV Contents
3.2.6
Discussion on ISDA
Without a Bias Term b
........... 73
3.3
Iterative Single Data Algorithm with an Explicit Bias Term
b
. 73
3.3.1
Iterative Single Data Algorithm for SVMs
Classification with a Bias Term
b
.................... 74
3.4
Performance of the Iterative Single Data Algorithm and
Comparisons
............................................ 80
3.5
Implementation Issues
................................... 83
3.5.1
Working-set Selection and Shrinking of ISDA for
Classification
..................................... 83
3.5.2
Computation of the Kernel Matrix and Caching of
ISDA for Classification
............................. 89
3.5.3
Implementation Details of ISDA for Regression
........ 92
3.6
Conclusions
............................................. 94
4
Feature Reduction with Support Vector Machines and
Application in
DNA Microarray
Analysis
.................. 97
4.1
Introduction
............................................ 97
4.2
Basics of Microarray Technology
.......................... 99
4.3
Some Prior Work
........................................101
4.3.1
Recursive Feature Elimination
with Support Vector Machines
......................101
4.3.2
Selection Bias and How to Avoid It
..................102
4.4
Influence of the Penalty Parameter
С
in RFE-SVMs
.........103
4.5
Gene Selection for the Colon Cancer and the Lymphoma
Data Sets
..............................................104
4.5.1
Results for Various
С
Parameters
...................104
4.5.2
Simulation Results with Different Preprocessing
Procedures
.......................................107
4.6
Comparison between RFE-SVMs and the Nearest Shrunken
Centroid Method
........................................112
4.6.1
Basic Concept of Nearest Shrunken Centroid Method
.. 112
4.6.2
Results on the Colon Cancer Data Set
and the Lymphoma Data Set
.......................115
4.7
Comparison of Genes Ranking with Different Algorithms
.....120
4.8
Conclusions
.............................................122
5
Semi-supervised Learning and Applications
................125
5.1
Introduction
............................................125
5.2
Gaussian Random Fields Model and Consistency Method
.....127
5.2.1
Gaussian Random Fields Model
.....................127
5.2.2
Global Consistency Model
..........................130
5.2.3
Random Walks on Graph
...........................133
5.3
An Investigation of the Effect of Unbalanced labeled Data on
CM and GRFM Algorithms
...............................136
5.3.1
Background and Test Settings
.......................136
Contents
XV
5.3.2
Results on the
Rec
Data Set
........................139
5.3.3
Possible Theoretical Explanations on the Effect of
Unbalanced Labeled Data
..........................139
5.4
Classifier Output Normalization: A Novel Decision Rule for
Semi-supervised Learning Algorithm
.......................142
5.5
Performance Comparison of Semi-supervised Learning
Algorithms
.............................................145
5.5.1
Low Density Separation: Integration of Graph-Based
Distances and VTSVM
............................146
5.5.2
Combining Graph-Based Distance with Manifold
Approaches
.......................................149
5.5.3
Test Data Sets
....................................150
5.5.4
Performance Comparison Between the LDS and the
Manifold Approaches
..............................152
5.5.5
Normalizatioin Steps and the Effect of
σ
.............154
5.6
Implementation of the Manifold Approaches
................154
5.6.1
Variants of the Manifold Approaches Implemented in
the Software Package SemiL
........................155
5.6.2
Implementation Details of SemiL
....................157
5.6.3
Conjugate Gradient Method with Box Constraints
.....162
5.6.4
Simulation Results on the MNIST Data Set
..........166
5.7
An Overview of Text Classification
........................167
5.8
Conclusions
.............................................171
6
Unsupervised Learning by Principal and Independent
Component Analysis
.......................................175
6.1
Principal Component Analysis
............................180
6.2
Independent Component Analysis
.........................197
6.3
Concluding Remarks
.....................................208
A Support Vector Machines
..................................209
A.I L2 Soft Margin Classifier
.................................210
A.2 L2 Soft Regressor
........................................211
A.3 Geometry and the Margin
................................213
В
Matlab
Code for ISDA Classification
......................217
С
Matlab
Code for ISDA Regression
.........................223
D
Matlab
Code for Conjugate Gradient Method with Box
Constraints
................................................229
E
Uncorrełatedness
and Independence
.......................233
XVI Contents
F
Independent Component
Analysis by Empirical
Estimation of Score Functions i.e., Probability Density
Functions
..................................................237
G SemiL
User Guide
.........................................241
G.I Installation
.............................................241
G.2 Input Data Format
......................................243
G.2.1 Raw Data Format:
................................243
G.3 Getting Started
.........................................244
СЗ.І
Design Stage
......................................245
References
.....................................................247
Index
..........................................................257
Kernel Based Algorithms for Mining Huge Data Sets is the first book
treating the fields of supervised, semi-supervised and unsupervised
machine learning collectively. The book presents both the theory
and the
aígorithms
for mining huge data sets by using support vector
machines (SVMs) in an iterative way. It demonstrates how kernel
based SVMs can be used for dimensionality reduction (feature
elimination) and shows the similarities and differences between the
two most popular unsupervised techniques, the principal component
analysis (PCA) and the independent component analysis
(ICA).
The book presents various examples, software, algorithmic solutions
enabling the reader to develop their own codes for solving the
problems. The book is accompanied by a website www.learning-
from-titinii om ior downloading both data and software presented in
it. The book focuses on a broad range of machine learning algorithms
in bioinformatics (gene microarrays), text-categorization, numerals
recognition, as well as in the images and audio signals de-mixing
(blind source separation) areas.
|
| adam_txt |
Contents
1
Introduction
. 1
1.1 An
Overview of Machine Learning
. 1
1.2
Challenges in Machine Learning
. 3
1.2.1
Solving Large-Scale SVMs
. 4
1.2.2
Feature Reduction with Support Vector Machines
. 5
1.2.3
Graph-Based Semi-supervised Learning Algorithms
.
С
1.2.4
Unsupervised Learning Based on Principle
of Redundancy Reduction
. 7
2
Support Vector Machines in Classification
and Regression
—
An Introduction
. 11
2.1
Basics of Learning from Data
. 12
2.2
Support Vector Machines in Classification and Regression
. 21
2.2.1
Linear Maximal Margin Classifier
for Linearly Separable Data
. 21
2.2.2
Linear Soft Margin Classifier for Overlapping Classes
. 32
2.2.3
The Nonlinear SVMs Classifier
. 36
2.2.4
Regression by Support Vector Machines
. 48
2.3
Implementation Issues
. 57
3
Iterative Single Data Algorithm for Kernel Machines
from Huge Data Sets: Theory and Performance
. 61
3.1
Introduction
. 61
3.2
Iterative Single Data Algorithm for Positive Definite Kernels
without Bias Term
6. 63
3.2.1
Kernel AdaTron in Classification
. 64
3.2.2
SMO
without Bias Term
b
in Classification
. 65
3.2.3
Kernel AdaTron in Regression
. 66
3.2.4
SMO
without Bias Term
b
in Regression
. 67
3.2.5
The Coordinate Ascent Based Learning for Nonlinear
Classification and Regression Tasks
. 68
XIV Contents
3.2.6
Discussion on ISDA
Without a Bias Term b
. 73
3.3
Iterative Single Data Algorithm with an Explicit Bias Term
b
. 73
3.3.1
Iterative Single Data Algorithm for SVMs
Classification with a Bias Term
b
. 74
3.4
Performance of the Iterative Single Data Algorithm and
Comparisons
. 80
3.5
Implementation Issues
. 83
3.5.1
Working-set Selection and Shrinking of ISDA for
Classification
. 83
3.5.2
Computation of the Kernel Matrix and Caching of
ISDA for Classification
. 89
3.5.3
Implementation Details of ISDA for Regression
. 92
3.6
Conclusions
. 94
4
Feature Reduction with Support Vector Machines and
Application in
DNA Microarray
Analysis
. 97
4.1
Introduction
. 97
4.2
Basics of Microarray Technology
. 99
4.3
Some Prior Work
.101
4.3.1
Recursive Feature Elimination
with Support Vector Machines
.101
4.3.2
Selection Bias and How to Avoid It
.102
4.4
Influence of the Penalty Parameter
С
in RFE-SVMs
.103
4.5
Gene Selection for the Colon Cancer and the Lymphoma
Data Sets
.104
4.5.1
Results for Various
С
Parameters
.104
4.5.2
Simulation Results with Different Preprocessing
Procedures
.107
4.6
Comparison between RFE-SVMs and the Nearest Shrunken
Centroid Method
.112
4.6.1
Basic Concept of Nearest Shrunken Centroid Method
. 112
4.6.2
Results on the Colon Cancer Data Set
and the Lymphoma Data Set
.115
4.7
Comparison of Genes' Ranking with Different Algorithms
.120
4.8
Conclusions
.122
5
Semi-supervised Learning and Applications
.125
5.1
Introduction
.125
5.2
Gaussian Random Fields Model and Consistency Method
.127
5.2.1
Gaussian Random Fields Model
.127
5.2.2
Global Consistency Model
.130
5.2.3
Random Walks on Graph
.133
5.3
An Investigation of the Effect of Unbalanced labeled Data on
CM and GRFM Algorithms
.136
5.3.1
Background and Test Settings
.136
Contents
XV
5.3.2
Results on the
Rec
Data Set
.139
5.3.3
Possible Theoretical Explanations on the Effect of
Unbalanced Labeled Data
.139
5.4
Classifier Output Normalization: A Novel Decision Rule for
Semi-supervised Learning Algorithm
.142
5.5
Performance Comparison of Semi-supervised Learning
Algorithms
.145
5.5.1
Low Density Separation: Integration of Graph-Based
Distances and VTSVM
.146
5.5.2
Combining Graph-Based Distance with Manifold
Approaches
.149
5.5.3
Test Data Sets
.150
5.5.4
Performance Comparison Between the LDS and the
Manifold Approaches
.152
5.5.5
Normalizatioin Steps and the Effect of
σ
.154
5.6
Implementation of the Manifold Approaches
.154
5.6.1
Variants of the Manifold Approaches Implemented in
the Software Package SemiL
.155
5.6.2
Implementation Details of SemiL
.157
5.6.3
Conjugate Gradient Method with Box Constraints
.162
5.6.4
Simulation Results on the MNIST Data Set
.166
5.7
An Overview of Text Classification
.167
5.8
Conclusions
.171
6
Unsupervised Learning by Principal and Independent
Component Analysis
.175
6.1
Principal Component Analysis
.180
6.2
Independent Component Analysis
.197
6.3
Concluding Remarks
.208
A Support Vector Machines
.209
A.I L2 Soft Margin Classifier
.210
A.2 L2 Soft Regressor
.211
A.3 Geometry and the Margin
.213
В
Matlab
Code for ISDA Classification
.217
С
Matlab
Code for ISDA Regression
.223
D
Matlab
Code for Conjugate Gradient Method with Box
Constraints
.229
E
Uncorrełatedness
and Independence
.233
XVI Contents
F
Independent Component
Analysis by Empirical
Estimation of Score Functions i.e., Probability Density
Functions
.237
G SemiL
User Guide
.241
G.I Installation
.241
G.2 Input Data Format
.243
G.2.1 Raw Data Format:
.243
G.3 Getting Started
.244
СЗ.І
Design Stage
.245
References
.247
Index
.257
Kernel Based Algorithms for Mining Huge Data Sets is the first book
treating the fields of supervised, semi-supervised and unsupervised
machine learning collectively. The book presents both the theory
and the
aígorithms
for mining huge data sets by using support vector
machines (SVMs) in an iterative way. It demonstrates how kernel
based SVMs can be used for dimensionality reduction (feature
elimination) and shows the similarities and differences between the
two most popular unsupervised techniques, the principal component
analysis (PCA) and the independent component analysis
(ICA).
The book presents various examples, software, algorithmic solutions
enabling the reader to develop their own codes for solving the
problems. The book is accompanied by a website www.learning-
from-titinii'om ior downloading both data and software presented in
it. The book focuses on a broad range of machine learning algorithms
in bioinformatics (gene microarrays), text-categorization, numerals
recognition, as well as in the images and audio signals de-mixing
(blind source separation) areas. |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Huang, Te-Ming Kecman, Vojislav 1948- Kopriva, Ivica |
| author_GND | (DE-588)131429884 (DE-588)111901995 (DE-588)131429892 |
| author_facet | Huang, Te-Ming Kecman, Vojislav 1948- Kopriva, Ivica |
| author_role | aut aut aut |
| author_sort | Huang, Te-Ming |
| author_variant | t m h tmh v k vk i k ik |
| building | Verbundindex |
| bvnumber | BV022448288 |
| callnumber-first | Q - Science |
| callnumber-label | Q325 |
| callnumber-raw | Q325.5 |
| callnumber-search | Q325.5 |
| callnumber-sort | Q 3325.5 |
| callnumber-subject | Q - General Science |
| classification_rvk | ST 300 ST 330 ST 530 |
| ctrlnum | (OCoLC)65207844 (DE-599)BVBBV022448288 |
| dewey-full | 006.3/12 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/12 |
| dewey-search | 006.3/12 |
| dewey-sort | 16.3 212 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| format | Book |
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| id | DE-604.BV022448288 |
| illustrated | Illustrated |
| index_date | 2024-07-02T17:35:24Z |
| indexdate | 2024-07-09T20:57:48Z |
| institution | BVB |
| isbn | 9783540316817 3540316817 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015656218 |
| oclc_num | 65207844 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | XVI, 260 S. Ill., zahlr. graph. Darst. 24 cm |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Huang, Te-Ming Verfasser (DE-588)131429884 aut Kernel based algorithms for mining huge data sets supervised, semi-supervised, and unsupervised learning Te-Ming Huang ; Vojislav Kecman ; Ivica Kopriva Berlin [u.a.] Springer 2006 XVI, 260 S. Ill., zahlr. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence 17 Literaturverz. S. 247 - 255 Data mining Kernel functions Machine learning Support-Vektor-Maschine (DE-588)4505517-8 gnd rswk-swf Teilüberwachtes Lernen (DE-588)4782452-9 gnd rswk-swf Unüberwachtes Lernen (DE-588)4580265-8 gnd rswk-swf Graphisches Modell (DE-588)4606156-3 gnd rswk-swf Faktorenanalyse (DE-588)4016338-6 gnd rswk-swf Überwachtes Lernen (DE-588)4580264-6 gnd rswk-swf Überwachtes Lernen (DE-588)4580264-6 s Support-Vektor-Maschine (DE-588)4505517-8 s DE-604 Teilüberwachtes Lernen (DE-588)4782452-9 s Graphisches Modell (DE-588)4606156-3 s Unüberwachtes Lernen (DE-588)4580265-8 s Faktorenanalyse (DE-588)4016338-6 s Kecman, Vojislav 1948- Verfasser (DE-588)111901995 aut Kopriva, Ivica Verfasser (DE-588)131429892 aut Studies in computational intelligence 17 (DE-604)BV020822171 17 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015656218&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015656218&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Huang, Te-Ming Kecman, Vojislav 1948- Kopriva, Ivica Kernel based algorithms for mining huge data sets supervised, semi-supervised, and unsupervised learning Studies in computational intelligence Data mining Kernel functions Machine learning Support-Vektor-Maschine (DE-588)4505517-8 gnd Teilüberwachtes Lernen (DE-588)4782452-9 gnd Unüberwachtes Lernen (DE-588)4580265-8 gnd Graphisches Modell (DE-588)4606156-3 gnd Faktorenanalyse (DE-588)4016338-6 gnd Überwachtes Lernen (DE-588)4580264-6 gnd |
| subject_GND | (DE-588)4505517-8 (DE-588)4782452-9 (DE-588)4580265-8 (DE-588)4606156-3 (DE-588)4016338-6 (DE-588)4580264-6 |
| title | Kernel based algorithms for mining huge data sets supervised, semi-supervised, and unsupervised learning |
| title_auth | Kernel based algorithms for mining huge data sets supervised, semi-supervised, and unsupervised learning |
| title_exact_search | Kernel based algorithms for mining huge data sets supervised, semi-supervised, and unsupervised learning |
| title_exact_search_txtP | Kernel based algorithms for mining huge data sets supervised, semi-supervised, and unsupervised learning |
| title_full | Kernel based algorithms for mining huge data sets supervised, semi-supervised, and unsupervised learning Te-Ming Huang ; Vojislav Kecman ; Ivica Kopriva |
| title_fullStr | Kernel based algorithms for mining huge data sets supervised, semi-supervised, and unsupervised learning Te-Ming Huang ; Vojislav Kecman ; Ivica Kopriva |
| title_full_unstemmed | Kernel based algorithms for mining huge data sets supervised, semi-supervised, and unsupervised learning Te-Ming Huang ; Vojislav Kecman ; Ivica Kopriva |
| title_short | Kernel based algorithms for mining huge data sets |
| title_sort | kernel based algorithms for mining huge data sets supervised semi supervised and unsupervised learning |
| title_sub | supervised, semi-supervised, and unsupervised learning |
| topic | Data mining Kernel functions Machine learning Support-Vektor-Maschine (DE-588)4505517-8 gnd Teilüberwachtes Lernen (DE-588)4782452-9 gnd Unüberwachtes Lernen (DE-588)4580265-8 gnd Graphisches Modell (DE-588)4606156-3 gnd Faktorenanalyse (DE-588)4016338-6 gnd Überwachtes Lernen (DE-588)4580264-6 gnd |
| topic_facet | Data mining Kernel functions Machine learning Support-Vektor-Maschine Teilüberwachtes Lernen Unüberwachtes Lernen Graphisches Modell Faktorenanalyse Überwachtes Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015656218&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015656218&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT huangteming kernelbasedalgorithmsformininghugedatasetssupervisedsemisupervisedandunsupervisedlearning AT kecmanvojislav kernelbasedalgorithmsformininghugedatasetssupervisedsemisupervisedandunsupervisedlearning AT koprivaivica kernelbasedalgorithmsformininghugedatasetssupervisedsemisupervisedandunsupervisedlearning |