Muutke küpsiste eelistusi

Gentle Introduction To Support Vector Machines In Biomedicine, A - Volume 2: Case Studies And Benchmarks [Kõva köide]

(New York Univ, Usa), (Clopinet, Usa), (New York Univ, Usa), (Vanderbilt Univ, Usa)
  • Formaat: Hardback, 212 pages
  • Ilmumisaeg: 06-May-2013
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9814324396
  • ISBN-13: 9789814324397
  • Formaat: Hardback, 212 pages
  • Ilmumisaeg: 06-May-2013
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9814324396
  • ISBN-13: 9789814324397
Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and cases studies (Volume 2).The proposed book follows the approach of “programmed learning” whereby material is presented in short sections called “frames”. Each frame consists of a very small amount of information to be learned, a multiple choice quiz, and answers to the quiz. The reader can proceed to the next frame only after verifying the correct answers to the current frame.
Part I Preliminaries
1(32)
Chapter 1 Introduction and Book Overview
3(6)
Organization of the Second Volume
3(5)
Acknowledgements
8(1)
Chapter 2 Methods Used in this Book
9(24)
i Basic Principles of Classification
9(4)
ii Main Ideas of the SVM Algorithm for Binary Classification
13(3)
iii Mathematical Formulation of SVMs
16(1)
iv Theoretical Underpinnings of SVMs
17(1)
v Model Selection and Accuracy Estimation
18(5)
vi Accuracy Metrics
23(1)
vii Statistical Comparison of Classification Results
24(3)
viii SVMs for Variable/Feature Selection
27(3)
ix Other Variable/Feature Selection Methods Used in this Book
30(3)
Part II Case Studies and Comparative Evaluation in High-Throughput Genomic Data
33(62)
Chapter 3 Application and Comparison of SVMs and Other Methods for Multicategory Microarray-Based Cancer Classification
35(16)
Methods
36(7)
Results
43(6)
Conclusions
49(2)
Chapter 4 Comparison of SVMs and Random Forests for Microarray-Based Cancer Classification
51(14)
Methods
52(5)
Results
57(5)
Conclusions
62(3)
Chapter 5 Comparison of SVMs and Kernel Ridge Regression for Microarray-Based Cancer Classification
65(8)
Zhiguo Li
Methods
65(4)
Results
69(3)
Conclusions
72(1)
Chapter 6 Application and Comparison of SVMs and Other Methods for Multicategory Classification in Microbiomics
73(14)
Mikael Henaff
Kranti Konganti
Varun Narendra
Alexander V. Alekseyenko
Methods
74(7)
Results
81(5)
Conclusions
86(1)
Chapter 7 Application to Assessment of Plasma Proteome Stability
87(8)
Methods
87(5)
Results
92(1)
Conclusions
93(2)
Part III Case Studies and Comparative Evaluation in Text Data
95(44)
Chapter 8 Application and Comparison of SVMs and Other Methods for Retrieving High-Quality Content-Specific Articles
97(10)
Yindalon Aphinyanaphongs
Methods
98(5)
Results
103(2)
Conclusions
105(2)
Chapter 9 Application and Comparison of SVMs and Other Methods for Identifying Unproven Cancer Treatments on the Web
107(6)
Yindalon Aphinyanaphongs
Methods
108(3)
Results
111(1)
Conclusions
112(1)
Chapter 10 Application to Predicting Future Article Citations
113(10)
Lawrence Fu
Methods
113(3)
Results
116(5)
Conclusions
121(2)
Chapter 11 Application to Classifying Instrumentality of Article Citations
123(8)
Lawrence Fu
Methods
124(3)
Results
127(3)
Conclusions
130(1)
Chapter 12 Application and Comparison of SVMs and Other Methods for Identifying Drug-Drug Interactions-Related Literature
131(8)
Stephany Duda
Methods
131(4)
Results
135(3)
Conclusions
138(1)
Part IV Case Studies with Clinical Data
139(22)
Chapter 13 Application to Predicting Clinical Laboratory Values
141(10)
Methods
141(6)
Results
147(3)
Conclusions
150(1)
Chapter 14 Application to Modeling Clinical Judgment and Guideline Compliance in the Diagnosis of Melanoma
151(10)
Andrea Sboner
Methods
151(4)
Results
155(5)
Conclusions
160(1)
Part V Other Comparative Evaluation Studies of Broad Applicability
161(30)
Chapter 15 Using SVMs for Causal Variable Selection
163(14)
Methods
164(3)
Results
167(9)
Conclusions
176(1)
Chapter 16 Application and Comparison of SVM-RFE and GLL Methods for Variable Selection for Classification
177(14)
Methods
178(4)
Results
182(5)
Conclusions
187(2)
Conclusions and Lessons Learned
189(2)
Bibliography 191(1)
Additional Reading for Presented Case Studies and Benchmarks 191(2)
List of References 193(6)
Index 199