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E-grāmata: Introduction To Pattern Recognition And Machine Learning

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(Indian Inst Of Science, India), (Indian Inst Of Science, India)
  • Formāts: 404 pages
  • Sērija : IISc Lecture Notes Series 5
  • Izdošanas datums: 22-Apr-2015
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • Valoda: eng
  • ISBN-13: 9789814656276
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Introduction To Pattern Recognition And Machine LearningPalielināt
  • Formāts: 404 pages
  • Sērija : IISc Lecture Notes Series 5
  • Izdošanas datums: 22-Apr-2015
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • Valoda: eng
  • ISBN-13: 9789814656276
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This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics neural networks, support vector machines and decision trees attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter.
About the Authors xiii
Preface xv
1 Introduction 1(36)
1 Classifiers: An Introduction
5(9)
2 An Introduction to Clustering
14(11)
3 Machine Learning
25(12)
2 Types of Data 37(38)
1 Features and Patterns
37(2)
2 Domain of a Variable
39(2)
3 Types of Features
41(9)
3.1 Nominal data
41(4)
3.2 Ordinal data
45(3)
3.3 Interval-valued variables
48(1)
3.4 Ratio variables
49(1)
3.5 Spatio-temporal data
49(1)
4 Proximity measures
50(25)
4.1 Fractional norms
56(1)
4.2 Are metrics essential?
57(2)
4.3 Similarity between vectors
59(2)
4.4 Proximity between spatial patterns
61(1)
4.5 Proximity between temporal patterns
62(1)
4.6 Mean dissimilarity
63(1)
4.7 Peak dissimilarity
63(1)
4.8 Correlation coefficient
64(1)
4.9 Dynamic Time Warping (DTW) distance
64(11)
3 Feature Extraction and Feature Selection 75(36)
1 Types of Feature Selection
76(2)
2 Mutual Information (MI) for Feature Selection
78(1)
3 Chi-square Statistic
79(2)
4 Goodman—Kruskal Measure
81(1)
5 Laplacian Score
81(2)
6 Singular Value Decomposition (SVD)
83(1)
7 Non-negative Matrix Factorization (NMF)
84(2)
8 Random Projections (RPs) for Feature Extraction
86(2)
8.1 Advantages of random projections
88(1)
9 Locality Sensitive Hashing (LSH)
88(2)
10 Class Separability
90(1)
11 Genetic and Evolutionary Algorithms
91(5)
11.1 Hybrid GA for feature selection
92(4)
12 Ranking for Feature Selection
96(7)
12.1 Feature selection based on an optimization formulation
97(2)
12.2 Feature ranking using F-score
99(1)
12.3 Feature ranking using linear support vector machine (SVM) weight vector
100(1)
12.4 Ensemble feature ranking
101(2)
12.5 Feature ranking using number of label changes
103(1)
13 Feature Selection for Time Series Data
103(8)
13.1 Piecewise aggregate approximation
103(1)
13.2 Spectral decomposition
104(1)
13.3 Wavelet decomposition
104(1)
13.4 Singular Value Decomposition (SVD)
104(1)
13.5 Common principal component loading based variable subset selection (CLeVer)
104(7)
4 Bayesian Learning 111(24)
1 Document Classification
111(2)
2 Naive Bayes Classifier
113(2)
3 Frequency-Based Estimation of Probabilities
115(2)
4 Posterior Probability
117(2)
5 Density Estimation
119(7)
6 Conjugate Priors
126(9)
5 Classification 135(42)
1 Classification Without Learning
135(4)
2 Classification in High-Dimensional Spaces
139(5)
2.1 Fractional distance metrics
141(2)
2.2 Shrinkage—divergence proximity (SDP)
143(1)
3 Random Forests
144(6)
3.1 Fuzzy random forests
148(2)
4 Linear Support Vector Machine (SVM)
150(6)
4.1 SVM—kNN
153(1)
4.2 Adaptation of cutting plane algorithm
154(1)
4.3 Nystrom approximated SVM
155(1)
5 Logistic Regression
156(3)
6 Semi-supervised Classification
159(8)
6.1 Using clustering algorithms
160(1)
6.2 Using generative models
160(1)
6.3 Using low density separation
161(1)
6.4 Using graph-based methods
162(2)
6.5 Using co-training methods
164(1)
6.6 Using self-training methods
165(1)
6.7 SVM for semi-supervised classification
166(1)
6.8 Random forests for semi-supervised classification
166(1)
7 Classification of Time-Series Data
167(10)
7.1 Distance-based classification
168(1)
7.2 Feature-based classification
169(1)
7.3 Model-based classification
170(7)
6 Classification using Soft Computing Techniques 177(38)
1 Introduction
177(1)
2 Fuzzy Classification
178(1)
2.1 Fuzzy k-nearest neighbor algorithm
179(1)
3 Rough Classification
179(3)
3.1 Rough set attribute reduction
180(1)
3.2 Generating decision rules
181(1)
4 GAs
182(13)
4.1 Weighting of attributes using GA
182(2)
4.2 Binary pattern classification using GA
184(1)
4.3 Rule-based classification using GAs
185(2)
4.4 Time series classification
187(1)
4.5 Using generalized Choquet integral with signed fuzzy measure for classification using GAs
187(4)
4.6 Decision tree induction using Evolutionary algorithms
191(4)
5 Neural Networks for Classification
195(7)
5.1 Multi-layer feed forward network with backpropagat ion
197(2)
5.2 Training a feedforward neural network using GAs
199(3)
6 Multi-label Classification
202(13)
6.1 Multi-label kNN (mL-kNN)
203(1)
6.2 Probabilistic classifier chains (PCC)
204(1)
6.3 Binary relevance (BR)
205(1)
6.4 Using label powersets (LP)
205(1)
6.5 Neural networks for Multi-label classification
206(3)
6.6 Evaluation of multi-label classification
209(6)
7 Data Clustering 215(48)
1 Number of Partitions
215(3)
2 Clustering Algorithms
218(23)
2.1 K-means algorithm
219(4)
2.2 Leader algorithm
223(2)
2.3 BIRCH: Balanced Iterative Reducing and Clustering using Hierarchies
225(5)
2.4 Clustering based on graphs
230(11)
3 Why Clustering?
241(5)
3.1 Data compression
241(1)
3.2 Outlier detection
242(1)
3.3 Pattern synthesis
243(3)
4 Clustering Labeled Data
246(9)
4.1 Clustering for classification
246(4)
4.2 Knowledge-based clustering
250(5)
5 Combination of Clusterings
255(8)
8 Soft Clustering 263(58)
1 Soft Clustering Paradigms
264(2)
2 Fuzzy Clustering
266(3)
2.1 Fuzzy K-means algorithm
267(2)
3 Rough Clustering
269(3)
3.1 Rough K-means algorithm
271(1)
4 Clustering Based on Evolutionary Algorithms
272(9)
5 Clustering Based on Neural Networks
281(1)
6 Statistical Clustering
282(11)
6.1 OKM algorithm
283(2)
6.2 EM-based clustering
285(8)
7 Topic Models
293(28)
7.1 Matrix factorization-based methods
295(1)
7.2 Divide-and-conquer approach
296(3)
7.3 Latent Semantic Analysis (LSA)
299(3)
7.4 SVD and PCA
302(5)
7.5 Probabilistic Latent Semantic Analysis (PLSA)
307(3)
7.6 Non-negative Matrix Factorization (NMF)
310(1)
7.7 LDA
311(5)
7.8 Concept and topic
316(5)
9 Application — Social and Information Networks 321(44)
1 Introduction
321(1)
2 Patterns in Graphs
322(4)
3 Identification of Communities in Networks
326(14)
3.1 Graph partitioning
328(1)
3.2 Spectral clustering
329(2)
3.3 Linkage-based clustering
331(1)
3.4 Hierarchical clustering
331(2)
3.5 Modularity optimization for partitioning graphs
333(7)
4 Link Prediction
340(7)
4.1 Proximity functions
341(6)
5 Information Diffusion
347(6)
5.1 Graph-based approaches
348(1)
5.2 Non-graph approaches
349(4)
6 Identifying Specific Nodes in a Social Network
353(2)
7 Topic Models
355(10)
7.1 Probabilistic latent semantic analysis (pLSA)
355(2)
7.2 Latent dirichlet allocation (LDA)
357(2)
7.3 Author-topic model
359(6)
Index 365
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