Introduction to machine learning:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge, Mass. [u.a.]
MIT Press
2014
|
| Edition: | 3. ed. |
| Series: | Adaptive computation and machine learning
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XXII, 613 S. graph. Darst. |
| ISBN: | 9780262028189 |
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| 100 | 1 | |a Alpaydın, Ethem |d 1966- |e Verfasser |0 (DE-588)134261046 |4 aut | |
| 245 | 1 | 0 | |a Introduction to machine learning |c Ethem Alpaydin |
| 250 | |a 3. ed. | ||
| 264 | 1 | |a Cambridge, Mass. [u.a.] |b MIT Press |c 2014 | |
| 300 | |a XXII, 613 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Adaptive computation and machine learning | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Machine learning | |
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Record in the Search Index
| _version_ | 1804152381608296448 |
|---|---|
| adam_text | Contents
Preface
xvii
Notations
xxi
1
Introduction
1
1.1
What Is
Machine
Learning? I
1.2
Examples of Machine Learning Applications
4
1.2.1
Learning Associations
4
1.2.2
Classification
5
1.2.3
Regression
9
1.2.4
Unsupervised Learning
11
1.2.5
Reinforcement Learning
13
1.3
Notes
14
1.4
Relevant Resources
17
1.5
Exercises
18
1.6
References
20
2
Supervised Learning
21
2.1
Learning a Class from Examples
21
2.2
Vapnik-Chervonenkis Dimension
27
2.3
Probably Approximately Correct Learning
29
2.4
Noise
30
2.5
Learning Multiple Classes
32
2.6
Regression
34
2.7
Model Selection and Generalization
37
2.8
Dimensions of a Supervised Machine Learning Algorithm
41
2.9
Notes
42
2.10
Exercises
43
2.11
References
47
3
Bayesian Decision Theory
49
3.1
Introduction
49
3.2
Classification
51
3.3
Losses and Risks
53
3.4
Discriminant Functions
55
3.5
Association Rules
56
3.6
Notes
59
3.7
Exercises
60
3.8
References
64
4
Parametric Methods
65
4.1
Introduction
65
4.2
Maximum Likelihood Estimation
66
4.2.1
Bernoulli Density
67
4.2.2
Multinomial Density
68
4.2.3
Gaussian (Normal) Density
68
4.3
Evaluating an Estimator: Bias and Variance
69
4.4
The
Bayes
Estimator
70
4.5
Parametric Classification
73
4.6
Regression
77
4.7
Tuning Model Complexity: Bias/Variance Dilemma
80
4.8
Model Selection Procedures
83
4.9
Notes
87
4.10
Exercises
88
4.11
References
90
5
Multivariate Methods
93
5.1
Multivariate Data
93
5.2
Parameter Estimation
94
5.3
Estimation of Missing Values
95
5.4
Multivariate Normal Distribution
96
5.5
Multivariate Classification
100
5.6
Tuning Complexity
106
5.7
Discrete Features
108
5.8
Multivariate Regression
109
5.9
Notes 111
5.10
Exercises
112
5.11
References
113
6
Dimensionality Reduction
115
6.1
Introduction
115
6.2
Subset Selection
116
6.3
Principal Component Analysis
120
6.4
Feature Embedding
127
6.5
Factor Analysis
130
6.6
Singular Value Decomposition and Matrix Factorization
135
6.7
Multidimensional Scaling
136
6.8
Linear Discriminant Analysis
140
6.9
Canonical Correlation Analysis
145
6.10 Isomap 148
6.11
Locally Linear Embedding
150
6.12
Laplacian Eigenmaps
153
6.13
Notes
155
6.14
Exercises
157
6.15
References
158
7
Clustering
161
7.1
Introduction
161
7.2
Mixture Densities
162
7.3
k-Means Clustering
163
7.4
Expectation-Maximization Algorithm
167
7.5
Mixtures of Latent Variable Models
172
7.6
Supervised Learning after Clustering
173
7.7
Spectral Clustering
175
7.8
Hierarchical Clustering
176
7.9
Choosing the Number of Clusters
178
7.10
Notes
179
7.11
Exercises
180
7.12
References
182
8
Nonparametric Methods
185
8.1
Introduction
185
8.2
Nonparametric Density Estimation
186
8.2.1
Histogram Estimator
187
8.2.2
Kernel Estimator
188
8.2.3
^-Nearest Neighbor Estimator
190
8.3
Generalization to Multivariate Data
192
8.4
Nonparametric Classification
193
8.5
Condensed Nearest Neighbor
194
8.6
Distance-Based Classification
196
8.7
Outlier Detection
199
8.8
Nonparametric Regression: Smoothing Models
201
8.8.1
Running Mean Smoother
201
8.8.2
Kernel Smoother
203
8.8.3
Running Line Smoother
204
iter
204
8.9
How to Choose the Smoothing Para
8.10
Notes
205
8.11
Exercises
208
8.12
References
210
9
Decision Trees
213
9.1
Introduction
213
9.2
Univariate Trees
215
9.2.1
Classification Trees
216
9.2.2
Regression Trees
220
9.3
Pruning
222
9.4
Rule Extraction from Trees
225
9.5
Learning Rules from Data
226
9.6
Multivariate Trees
230
9.7
Notes
232
9.8
Exercises
235
9.9
References
237
10
Linear Discrimination
239
10.1
Introduction
239
10.2
Generalizing the Linear Model
241
10.3
Geometry of the Linear Discriminant
242
10.3.1
Two Classes
242
10.3.2
Multiple Classes
244
10.4
Pairwise Separation
246
10.5
Parametric Discrimination Revisited
247
10.6
Gradient Descent
248
10.7
Logistic Discrimination
250
10.7.1
Two Classes
250
10.7.2
Multiple Classes
254
10.8
Discrimination by Regression
257
10.9
Learning to Rank
260
10.10
Notes
263
10.11
Exercises
263
10.12
References
266
11
Multilayer
Percepir
ons
267
11.1
Introduction
267
11.1.1
Understanding the Brain
268
11.1.2
Neural Networks as a Paradigm for Parallel
Processing
269
11.2
The Perceptron
271
11.3
Training a Perceptron
274
11.4
Learning Boolean Functions
277
11.5
Multilayer
Percep
trons
279
11.6 MLP
as a Universal Approximator
281
11.7
Backpropagation Algorithm
283
11.7.1
Nonlinear Regression
284
11.7.2
Two-Class Discrimination
286
11.7.3
Multiclass Discrimination
288
11.7.4
Multiple Hidden Layers
290
11.8
Training Procedures
290
11.8.1
Improving Convergence
290
11.8.2
Overtraining
291
11.8.3
Structuring the Network
292
11.8.4
Hints
295
11.9
Tuning the Network Size
297
11.10
Bayesian View of Learning
300
11.11
Dimensionality Reduction
301
11.12
Learning Time
304
11.12.1
Time Delay Neural Networks
304
11.12.2
Recurrent Networks
305
11.13
Deep Learning
306
11.14
Notes
309
11.15
Exercises
311
11.16
References
313
12
Local Models
317
12.1
Introduction
317
12.2
Competitive Learning
318
12.2.1
Online k-Means
318
12.2.2
Adaptive
Resonance Theory
323
12.2.3
Self-Organizing Maps
324
12.3
Radial Basis Functions
326
12.4
Incorporating Rule-Based Knowledge
332
12.5
Normalized Basis Functions
333
12.6
Competitive Basis Functions
335
12.7
Learning Vector Quantization
338
12.8
The Mixture of Experts
338
12.8.1
Cooperative Experts
341
12.8.2
Competitive Experts
342
12.9
Hierarchical Mixture of Experts
342
12.10
Notes
343
12.11
Exercises
344
12.12
References
347
13
Kernel Machines
349
13.1
Introduction
349
13.2
Optimal Separating
Hyperplane 351
13.3
The Nonseparable Case: Soft Margin
Hyperplane 355
13.4
v-SVM
358
13.5
Kernel Trick
359
13.6
Vectorial
Kernels
361
13.7
Defining Kernels
364
13.8
Multiple Kernel Learning
365
13.9
Multiclass Kernel Machines
367
13.10
Kernel Machines for Regression
368
13.11
Kernel Machines for Ranking
373
13.12
One-Class Kernel Machines
374
13.13
Large Margin Nearest Neighbor Classifier
377
13.14
Kernel Dimensionality Reduction
379
13.15
Notes
380
13.16
Exercises
382
13.17
References
383
14
Graphical Models
387
14.1
Introduction
387
14.2
Canonical Cases for Conditional Independence
389
14.3
Generative Models
396
14.4
d-Separation
399
14.5
Belief Propagation
399
14.5.1
Chains
400
14.5.2
Trees
402
14.5.3
Polytrees
404
14.5.4
Junction Trees
406
14.6
Undirected Graphs: Markov Random Fields
407
14.7
Learning the Structure of a Graphical Model
410
14.8
Influence Diagrams
411
14.9
Notes
412
14.10
Exercises
413
14.11
References
415
15
Hidden Markov Models
417
15.1
Introduction
417
15.2
Discrete Markov Processes
418
15.3
Hidden Markov Models
421
15.4
Three Basic Problems of HMMs
423
15.5
Evaluation Problem
423
15.6
Finding the State Sequence
427
15.7
Learning Model Parameters
429
15.8
Continuous Observations
432
15.9
The
HMM
as a Graphical Model
433
15.10
Model Selection in HMMs
436
15.11
Notes
438
15.12
Exercises
440
15.13
References
443
16
Bayesian Estimation
445
16.1
Introduction
445
16.2
Bayesian Estimation of the Parameters of a Discrete
Distribution
449
16.2.1
К
> 2
States: Dirichlet Distribution
449
16.2.2
К
= 2
States: Beta Distribution
450
16.3
Bayesian Estimation of the Parameters of a Gaussian
Distribution
451
16.3.1
Univariate Case: Unknown Mean, Known
Variance
451
16.3.2
Univariate Case: Unknown Mean, Unknown
Variance
453
16.3.3
Multivariate Case: Unknown Mean, Unknown
Covariance
455
16.4
Bayesian Estimation of the Parameters of a Function
456
16.4.1
Regression
456
16.4.2
Regression with Prior on Noise Precision
460
16.4.3
The Use of Basis/Kernel Functions
461
16.4.4
Bayesian Classification
463
16.5
Choosing a Prior
466
16.6
Bayesian Model Comparison
467
16.7
Bayesian Estimation of a Mixture Model
470
16.8
Nonparametric Bayesian Modeling
473
16.9
Gaussian Processes
474
16.10
Dirichlet Processes and Chinese Restaurants
478
16.11
Latent Dirichlet Allocation
480
16.12
Beta Processes and Indian Buffets
482
16.13
Notes
483
16.14
Exercises
484
16.15
References
485
17
Combining Multiple Learners
487
17.1
Rationale
487
17.2
Generating Diverse Learners
488
17.3
Model Combination Schemes
491
17.4
Voting
492
17.5
Error-Correcting Output Codes
496
17.6
Bagging
498
17.7
Boosting
499
17.8
The Mixture of Experts Revisited
502
17.9
Stacked Generalization
504
17.10
Fine-Tuning an Ensemble
505
17.10.1
Choosing a Subset of the Ensemble
506
17.10.2
Constructing Metalearners
506
17.11
Cascading
507
17.12
Notes
509
17.13
Exercises
511
17.14
References
513
18
Reinforcement Learning
517
18.1
Introduction
517
18.2
Single State Case:
Х
-Armed Bandit
519
18.3
Elements of Reinforcement Learning
520
18.4
Model-Based Learning
523
18.4.1
Value Iteration
523
18.4.2
Policy Iteration
524
18.5
Temporal Difference Learning
525
18.5.1
Exploration Strategies
525
18.5.2
Deterministic Rewards and Actions
526
18.5.3
Nondeterministic Rewards and Actions
527
18.5.4
Eligibility Traces
530
18.6
Generalization
531
18.7
Partially Observable States
534
18.7.1
The Setting
534
18.7.2
Example: The Tiger Problem
536
18.8
Notes
541
18.9
Exercises
542
18.10
References
544
19
Design and Analysis of Machine Learning Experiments
547
19.1
Introduction
547
19.2
Factors, Response, and Strategy of Experimentation
550
19.3
Response Surface Design
553
19.4
Randomization, Replication, and Blocking
554
19.5
Guidelines for Machine Learning Experiments
555
19.6
Cross-Validation and Resampling Methods
558
19.6.1
K-Fold Cross-Validation
559
19.6.2 5
χ
2
Cross-Validation
560
19.6.3
Bootstrapping
561
19.7
Measuring Classifier Performance
561
19.8
Interval Estimation
564
19.9
Hypothesis Testing
568
19.10
Assessing a Classification Algorithm s Performance
570
19.10.1
Binomial Test
571
19.10.2
Approximate Normal Test
572
19.10.3
t
Test
572
19.11
Comparing Two Classification Algorithms
573
19.11.1
McNemaťs
Test
573
19.11.2
К-ЇоШ
Cross-Validated Paired
f
Test
573
19.11.3 5x2 cv
Paired
t
Test
574
19.11.4 5
x
2 cv
Paired
F
Test
575
19.12
Comparing Multiple Algorithms: Analysis of Variance
576
19.13
Comparison over Multiple
Datasets
580
19.13.1
Comparing Two Algorithms
581
19.13.2
Multiple Algorithms
5 8 3
19.14
Multivariate Tests
584
19.14.1
Comparing Two Algorithms
585
19.14.2
Comparing Multiple Algorithms
586
19.15
Notes
587
19.16
Exercises
588
19.17
References
590
A Probability
593
A.I Elements of Probability
593
A.
1.1
Axioms of Probability
594
A.
1.2
Conditional Probability
5 94
A.
2
Random Variables
595
A.2.1 Probability Distribution and Density Functions
595
A.
2.2
Joint Distribution and Density Functions
596
A.2.3 Conditional Distributions
596
A.2.4
Bayes
Rule
597
A.2.5 Expectation
597
A.2.6 Variance
598
A.2.7 Weak Law of Large Numbers
599
A.3 Special Random Variables
599
A.3.1 Bernoulli Distribution
599
A.3.
2
Binomial Distribution
600
A.
3.3
Multinomial Distribution
600
A.3.4 Uniform Distribution
600
A.
3.5
Normal (Gaussian) Distribution
601
A.3.
6
Chi-Square Distribution
602
A.3.
7
f
Distribution
603
A.3.8
F
Distribution
603
A.4 References
603
Index
605
INTRODUCTION
TO
MACHINE
LEARNING machine learning
third edition
ETHEM ALPAYDIN
The goal of machine learning is to program computers to use example data or past experience to solve a given problem.
Many successful applications of machine learning exist already, including systems that analyze past sales data to predict
customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowl¬
edge from bioinformatics data. Introduction to Machine learning is a comprehensive textbook on the subject, covering a
broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning;
Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov
models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.
Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third
edition of Introduction to Machim Learning reflects this shift, with added support for beginners, including selected solutions
for exercises and additional example data sets (with code available online). Other substantial changes include discussions
of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral
methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric
approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations
in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will
also be of interest to professionals who are concerned with the application of machine learning methods.
ETHEM AiPAYDiN is a Professor in the Department of Computer Engineering at
Bogaziçi
University, Istanbul.
ADAPTIVE COMPUTATION AND MACHINE LEARNING SERIES
Ethem Alpaydin s Introduction to Machine learning provides a nice blending of the topical coverage of machine learning
(à la
Tom Mitchell) with formal probabilistic foundations
(à la
Christopher Bishop). This newly updated version now
introduces some of the most recent and important topics in machine learning (e.g., spectral methods, deep learning, and
learning to rank) to students and researchers of this critically important and expanding field.
—John W. Sheppard, Professor of Computer Science, Montana State University
I have used Introduction to Machine karning for several years in my graduate Machine Learning course. The book pro¬
vides an ideal balance of theory and practice, and with this third edition, extends coverage to many new state-of-the-art
algorithms. I look forward to using this edition in my next Machine Learning course.
—Larry Holder, Professor of Electrical Engineering and Computer Science, Washington State University
This volume is both a complete and accessible introduction to the machine learning world. This is a Swiss Army knife
book for this rapidly evolving subject. Although intended as an introduction, it will be useful not only for students but
for any professional looking for a comprehensive book in this field. Newcomers will find clearly explained concepts and
experts will find a source for new references and ideas.
—Hilario Gómez-Moreno,
IEEE Senior Member, University of
Alcalá,
Spain
|
| any_adam_object | 1 |
| author | Alpaydın, Ethem 1966- |
| author_GND | (DE-588)134261046 |
| author_facet | Alpaydın, Ethem 1966- |
| author_role | aut |
| author_sort | Alpaydın, Ethem 1966- |
| author_variant | e a ea |
| building | Verbundindex |
| bvnumber | BV041980898 |
| 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 304 |
| classification_tum | DAT 708f |
| ctrlnum | (OCoLC)892914802 (DE-599)BVBBV041980898 |
| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 3. ed. |
| format | Book |
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| genre_facet | Lehrbuch |
| id | DE-604.BV041980898 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:09:49Z |
| institution | BVB |
| isbn | 9780262028189 |
| language | English |
| lccn | 014007214 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027423356 |
| oclc_num | 892914802 |
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| physical | XXII, 613 S. graph. Darst. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | MIT Press |
| record_format | marc |
| series2 | Adaptive computation and machine learning |
| spelling | Alpaydın, Ethem 1966- Verfasser (DE-588)134261046 aut Introduction to machine learning Ethem Alpaydin 3. ed. Cambridge, Mass. [u.a.] MIT Press 2014 XXII, 613 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Adaptive computation and machine learning Includes bibliographical references and index Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Maschinelles Lernen (DE-588)4193754-5 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027423356&sequence=000005&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027423356&sequence=000006&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Alpaydın, Ethem 1966- Introduction to machine learning Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4123623-3 |
| title | Introduction to machine learning |
| title_auth | Introduction to machine learning |
| title_exact_search | Introduction to machine learning |
| title_full | Introduction to machine learning Ethem Alpaydin |
| title_fullStr | Introduction to machine learning Ethem Alpaydin |
| title_full_unstemmed | Introduction to machine learning Ethem Alpaydin |
| title_short | Introduction to machine learning |
| title_sort | introduction to machine learning |
| topic | Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning Maschinelles Lernen Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027423356&sequence=000005&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=027423356&sequence=000006&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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