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Informatics and Machine Learning: From Martingales to Metaheuristics 요약정보 및 구매

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지은이 Stephen Winters-Hilt
발행년도 2022-01-01
판수 1판
페이지 582
ISBN 9781119716747
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  • Informatics and Machine Learning: From Martingales to Metaheuristics
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  • DESCRIPTION

    Informatics and Machine Learning

    Discover a thorough exploration of how to use computational, algorithmic, statistical, and informatics methods to analyze digital data

    Informatics and Machine Learning: From Martingales to Metaheuristics delivers an interdisciplinary presentation on how analyze any data captured in digital form. The book describes how readers can conduct analyses of text, general sequential data, experimental observations over time, stock market and econometric histories, or symbolic data, like genomes. It contains large amounts of sample code to demonstrate the concepts contained within and assist with various levels of project work.

    The book offers a complete presentation of the mathematical underpinnings of a wide variety of forms of data analysis and provides extensive examples of programming implementations. It is based on two decades worth of the distinguished author’s teaching and industry experience.

    • A thorough introduction to probabilistic reasoning and bioinformatics, including Python shell scripting to obtain data counts, frequencies, probabilities, and anomalous statistics, or use with Bayes’ rule
    • An exploration of information entropy and statistical measures, including Shannon entropy, relative entropy, maximum entropy (maxent), and mutual information
    • A practical discussion of ad hoc, ab initio, and bootstrap signal acquisition methods, with examples from genome analytics and signal analytics

    Perfect for undergraduate and graduate students in machine learning and data analytics programs, Informatics and Machine Learning: From Martingales to Metaheuristics will also earn a place in the libraries of mathematicians, engineers, computer scientists, and life scientists with an interest in those subjects.

  • Preface xv

    1 Introduction 1

    1.1 Data Science: Statistics, Probability, Calculus … Python (or Perl) and Linux 2

    1.2 Informatics and Data Analytics 3

    1.3 FSA-Based Signal Acquisition and Bioinformatics 4

    1.4 Feature Extraction and Language Analytics 7

    1.5 Feature Extraction and Gene Structure Identification 8

    1.5.1 HMMs for Analysis of Information Encoding Molecules 11

    1.5.2 HMMs for Cheminformatics and Generic Signal Analysis 11

    1.6 Theoretical Foundations for Learning 13

    1.7 Classification and Clustering 13

    1.8 Search 14

    1.9 Stochastic Sequential Analysis (SSA) Protocol (Deep Learning Without NNs) 15

    1.9.1 Stochastic Carrier Wave (SCW) Analysis – Nanoscope Signal Analysis 18

    1.9.2 Nanoscope Cheminformatics – A Case Study for Device “Smartening” 19

    1.10 Deep Learning using Neural Nets 20

    1.11 Mathematical Specifics and Computational Implementations 21

    2 Probabilistic Reasoning and Bioinformatics 23

    2.1 Python Shell Scripting 23

    2.1.1 Sample Size Complications 33

    2.2 Counting, the Enumeration Problem, and Statistics 34

    2.3 From Counts to Frequencies to Probabilities 35

    2.4 Identifying Emergent/Convergent Statistics and Anomalous Statistics 35

    2.5 Statistics, Conditional Probability, and Bayes’ Rule 37

    2.5.1 The Calculus of Conditional Probabilities: The Cox Derivation 37

    2.5.2 Bayes’ Rule 38

    2.5.3 Estimation Based on Maximal Conditional Probabilities 38

    2.6 Emergent Distributions and Series 39

    2.6.1 The Law of Large Numbers (LLN) 39

    2.6.2 Distributions 39

    2.6.3 Series 42

    2.7 Exercises 42

    3 Information Entropy and Statistical Measures 47

    3.1 Shannon Entropy, Relative Entropy, Maxent, Mutual Information 48

    3.1.1 The Khinchin Derivation 49

    3.1.2 Maximum Entropy Principle 49

    3.1.3 Relative Entropy and Its Uniqueness 51

    3.1.4 Mutual Information 51

    3.1.5 Information Measures Recap 52

    3.2 Codon Discovery from Mutual Information Anomaly 58

    3.3 ORF Discovery from Long-Tail Distribution Anomaly 66

    3.3.1 Ab initio Learning with smORF’s, Holistic Modeling, and Bootstrap Learning 69

    3.4 Sequential Processes and Markov Models 72

    3.4.1 Markov Chains 73

    3.5 Exercises 75

    Ad HocAb Initio, and Bootstrap Signal Acquisition Methods 77

    4.1 Signal Acquisition, or Scanning, at Linear Order Time-Complexity 77

    4.2 Genome Analytics: The Gene-Finder 80

    4.3 Objective Performance Evaluation: Sensitivity and Specificity 93

    4.4 Signal Analytics: The Time-Domain Finite State Automaton (tFSA) 93

    4.4.1 tFSA Spike Detector 95

    4.4.2 tFSA-Based Channel Signal Acquisition Methods with Stable Baseline 98

    4.4.3 tFSA-Based Channel Signal Acquisition Methods Without Stable Baseline 103

    4.5 Signal Statistics (Fast): Mean, Variance, and Boxcar Filter 107

    4.5.1 Efficient Implementations for Statistical Tools (O(L)) 109

    4.6 Signal Spectrum: Nyquist Criterion, Gabor Limit, Power Spectrum 110

    4.6.1 Nyquist Sampling Theorem 110

    4.6.2 Fourier Transforms, and Other Classic Transforms 110

    4.6.3 Power Spectral Density 111

    4.6.4 Power-Spectrum-Based Feature Extraction 111

    4.6.5 Cross-Power Spectral Density 112

    4.6.6 AM/FM/PM Communications Protocol 112

    4.7 Exercises 112

    5 Text Analytics 125

    5.1 Words 125

    5.1.1 Text Acquisition: Text Scraping and Associative Memory 125

    5.1.2 Word Frequency Analysis: Machiavelli’s Polysemy on Fortuna and Virtu 130

    5.1.3 Word Frequency Analysis: Coleridge’s Hidden Polysemy on Logos 139

    5.1.4 Sentiment Analysis 143

    5.2 Phrases – Short (Three Words) 145

    5.2.1 Shakespearean Insult Generation – Phrase Generation 147

    5.3 Phrases – Long (A Line or Sentence) 150

    5.3.1 Iambic Phrase Analysis: Shakespeare 150

    5.3.2 Natural Language Processing 152

    5.3.3 Sentence and Story Generation: Tarot 152

    5.4 Exercises 153

    6 Analysis of Sequential Data Using HMMs 155

    6.1 Hidden Markov Models (HMMs) 155

    6.1.1 Background and Role in Stochastic Sequential Analysis (SSA) 155

    6.1.2 When to Use a Hidden Markov Model (HMM)? 160

    6.1.3 Hidden Markov Models (HMMs) – Standard Formulation and Terms 161

    6.2 Graphical Models for Markov Models and Hidden Markov Models 162

    6.2.1 Hidden Markov Models 162

    6.2.2 Viterbi Path 163

    6.2.3 Forward and Backward Probabilities 164

    6.2.4 HMM: Maximum Likelihood discrimination 165

    6.2.5 Expectation/Maximization (Baum–Welch) 166

    6.3 Standard HMM Weaknesses and their GHMM Fixes 168

    6.4 Generalized HMMs (GHMMs – “Gems”): Minor Viterbi Variants 171

    6.4.1 The Generic HMM 171

    6.4.2 pMM/SVM 171

    6.4.3 EM and Feature Extraction via EVA Projection 172

    6.4.4 Feature Extraction via Data Absorption (a.k.a. Emission Inversion) 174

    6.4.5 Modified AdaBoost for Feature Selection and Data Fusion 176

    6.5 HMM Implementation for Viterbi (in C and Perl) 179

    6.6 Exercises 206

    7 Generalized HMMs (GHMMs): Major Viterbi Variants 207

    7.1 GHMMs: Maximal Clique for Viterbi and Baum–Welch 207

    7.2 GHMMs: Full Duration Model 216

    7.2.1 HMM with Duration (HMMD) 216

    7.2.2 Hidden Semi-Markov Models (HSMM) with sid-information 220

    7.2.3 HMM with Binned Duration (HMMBD) 224

    7.3 GHMMs: Linear Memory Baum–Welch Algorithm 228

    7.4 GHMMs: Distributable Viterbi and Baum–Welch Algorithms 230

    7.4.1 Distributed HMM processing via “Viterbi-overlap-chunking” with GPU speedup 230

    7.4.2 Relative Entropy and Viterbi Scoring 231

    7.5 Martingales and the Feasibility of Statistical Learning (further details in Appendix) 232

    7.6 Exercises 234

    8 Neuromanifolds and the Uniqueness of Relative Entropy 235

    8.1 Overview 235

    8.2 Review of Differential Geometry 236

    8.2.1 Differential Topology – Natural Manifold 236

    8.2.2 Differential Geometry – Natural Geometric Structures 240

    8.3 Amari’s Dually Flat Formulation 243

    8.3.1 Generalization of Pythagorean Theorem 246

    8.3.2 Projection Theorem and Relation Between Divergence and Link Formalism 246

    8.4 Neuromanifolds 247

    8.5 Exercises 250

    9 Neural Net Learning and Loss Bounds Analysis 253

    9.1 Brief Introduction to Neural Nets (NNs) 254

    9.1.1 Single Neuron Discriminator 254

    9.1.2 Neural Net with Back-Propagation 258

    9.2 Variational Learning Formalism and Use in Loss Bounds Analysis 261

    9.2.1 Variational Basis for Update Rule 261

    9.2.2 Review and Generalization of GD Loss Bounds Analysis 262

    9.2.3 Review of the EG Loss Bounds Analysis 266

    9.3 The “sinh−1(ω)” link algorithm (SA) 266

    9.3.1 Motivation for “sinh−1(ω)” link algorithm (SA) 266

    9.3.2 Relation of sinh Link Algorithm to the Binary Exponentiated Gradient Algorithm 268

    9.4 The Loss Bounds Analysis for sinh−1(ω) 269

    9.4.1 Loss Bounds Analysis Using the Taylor Series Approach 273

    9.4.2 Loss Bounds Analysis Using Taylor Series for the sinh Link (SA) Algorithm 275

    9.5 Exercises 277

    10 Classification and Clustering 279

    10.1 The SVM Classifier – An Overview 281

    10.2 Introduction to Classification and Clustering 282

    10.2.1 Sum of Squared Error (SSE) Scoring 286

    10.2.2 K-Means Clustering (Unsupervised Learning) 286

    10.2.3 k-Nearest Neighbors Classification (Supervised Learning) 292

    10.2.4 The Perceptron Recap (See Chapter 9 for Details) 295

    10.3 Lagrangian Optimization and Structural Risk Minimization (SRM) 296

    10.3.1 Decision Boundary and SRM Construction Using Lagrangian 296

    10.3.2 The Theory of Classification 301

    10.3.3 The Mathematics of the Feasibility of Learning 303

    10.3.4 Lagrangian Optimization 306

    10.3.5 The Support Vector Machine (SVM) – Lagrangian with SRM 308

    10.3.6 Kernel Construction Using Polarization 310

    10.3.7 SVM Binary Classifier Derivation 312

    10.4 SVM Binary Classifier Implementation 318

    10.4.1 Sequential Minimal Optimization (SMO) 318

    10.4.2 Alpha-Selection Variants 320

    10.4.3 Chunking on Large Datasets: O(N2) ➔ n O(N2/n2) = O(N2)/n 320

    10.4.4 Support Vector Reduction (SVR) 331

    10.4.5 Code Examples (in OO Perl) 335

    10.5 Kernel Selection and Tuning Metaheuristics 346

    10.5.1 The “Stability” Kernels 346

    10.5.2 Derivation of “Stability” Kernels 349

    10.5.3 Entropic and Gaussian Kernels Relate to Unique, Minimally Structured, Information Divergence and Geometric Distance Measures 351

    10.5.4 Automated Kernel Selection and Tuning 353

    10.6 SVM Multiclass from Decision Tree with SVM Binary Classifiers 356

    10.7 SVM Multiclass Classifier Derivation (Multiple Decision Surface) 359

    10.7.1 Decomposition Method to Solve the Dual 361

    10.7.2 SVM Speedup via Differentiating BSVs and SVs 362

    10.8 SVM Clustering 364

    10.8.1 SVM-External Clustering 365

    10.8.2 Single-Convergence SVM-Clustering: Comparative Analysis 368

    10.8.3 Stabilized, Single-Convergence Initialized, SVM-External Clustering 375

    10.8.4 Stabilized, Multiple-Convergence, SVM-External Clustering 379

    10.8.5 SVM-External Clustering – Algorithmic Variants 381

    10.9 Exercises 385

    11 Search Metaheuristics 389

    11.1 Trajectory-Based Search Metaheuristics 389

    11.1.1 Optimal-Fitness Configuration Trajectories – Fitness Function Known and Sufficiently Regular 390

    11.1.2 Optimal-Fitness Configuration Trajectories – Fitness Function not Known 392

    11.1.3 Fitness Configuration Trajectories with Nonoptimal Updates 397

    11.2 Population-Based Search Metaheuristics 399

    11.2.1 Population with Evolution 400

    11.2.2 Population with Group Interaction – Swarm Intelligence 402

    11.2.3 Population with Indirect Interaction via Artifact 403

    11.3 Exercises 404

    12 Stochastic Sequential Analysis (SSA) 407

    12.1 HMM and FSA-Based Methods for Signal Acquisition and Feature Extraction 408

    12.2 The Stochastic Sequential Analysis (SSA) Protocol 410

    12.2.1 (Stage 1) Primitive Feature Identification 415

    12.2.2 (Stage 2) Feature Identification and Feature Selection 416

    12.2.3 (Stage 3) Classification 418

    12.2.4 (Stage 4) Clustering 418

    12.2.5 (All Stages) Database/Data-Warehouse System Specification 419

    12.2.6 (All Stages) Server-Based Data Analysis System Specification 420

    12.3 Channel Current Cheminformatics (CCC) Implementation of the Stochastic Sequential Analysis (SSA) Protocol 420

    12.4 SCW for Detector Sensitivity Boosting 423

    12.4.1 NTD with Multiple Channels (or High Noise) 424

    12.4.2 Stochastic Carrier Wave 426

    12.5 SSA for Deep Learning 430

    12.6 Exercises 431

    13 Deep Learning Tools – TensorFlow 433

    13.1 Neural Nets Review 433

    13.1.1 Summary of Single Neuron Discriminator 433

    13.1.2 Summary of Neural Net Discriminator and Back-Propagation 433

    13.2 TensorFlow from Google 435

    13.2.1 Installation/Setup 436

    13.2.2 Example: Character Recognition 437

    13.2.3 Example: Language Translation 440

    13.2.4 TensorBoard and the TensorFlow Profiler 441

    13.2.5 Tensor Cores 444

    13.3 Exercises 444

    14 Nanopore Detection – A Case Study 445

    14.1 Standard Apparatus 447

    14.1.1 Standard Operational and Physiological Buffer Conditions 448

    14.1.2 α-Hemolysin Channel Stability – Introduction of Chaotropes 448

    14.2 Controlling Nanopore Noise Sources and Choice of Aperture 449

    14.3 Length Resolution of Individual DNA Hairpins 451

    14.4 Detection of Single Nucleotide Differences (Large Changes in Structure) 454

    14.5 Blockade Mechanism for 9bphp 455

    14.6 Conformational Kinetics on Model Biomolecules 459

    14.7 Channel Current Cheminformatics 460

    14.7.1 Power Spectra and Standard EE Signal Analysis 460

    14.7.2 Channel Current Cheminformatics for Single-Biomolecule/Mixture Identifications 462

    14.7.3 Channel Current Cheminformatics: Feature Extraction by HMM 464

    14.7.4 Bandwidth Limitations 465

    14.8 Channel-Based Detection Mechanisms 467

    14.8.1 Partitioning and Translocation-Based ND Biosensing Methods 467

    14.8.2 Transduction Versus Translation 468

    14.8.3 Single-Molecule Versus Ensemble 469

    14.8.4 Biosensing with High Sensitivity in Presence of Interference 470

    14.8.5 Nanopore Transduction Detection Methods 471

    14.9 The NTD Nanoscope 474

    14.9.1 Nanopore Transduction Detection (NTD) 475

    14.9.2 NTD: A Versatile Platform for Biosensing 479

    14.9.3 NTD Platform 481

    14.9.4 NTD Operation 484

    14.9.5 Driven Modulations 487

    14.9.6 Driven Modulations with Multichannel Augmentation 490

    14.10 NTD Biosensing Methods 495

    14.10.1 Model Biosensor Based on Streptavidin and Biotin 495

    14.10.2 Model System Based on DNA Annealing 501

    14.10.3 Y-Aptamer with Use of Chaotropes to Improve Signal Resolution 506

    14.10.4 Pathogen Detection, miRNA Detection, and miRNA Haplotyping 508

    14.10.5 SNP Detection 510

    14.10.6 Aptamer-Based Detection 512

    14.10.7 Antibody-Based Detection 512

    14.11 Exercises 516

    Appendix A: Python and Perl System Programming in Linux 519

    A.1 Getting Linux and Python in a Flash (Drive) 519

    A.2 Linux and the Command Shell 520

    A.3 Perl Review: I/O, Primitives, String Handling, Regex 521

    Appendix B: Physics 529

    B.1 The Calculus of Variations 529

    Appendix C: Math 531

    C.1 Martingales 531

    C.2 Hoeffding Inequality 537

    References 541

    Index 559

  • Stephen Winters-Hilt is Sole Proprietor at Meta Logos Systems, specializing in Machine Learning, Signal Analysis, Financial Analytics, and Bioinformatics. He obtained a PhD in Theoretical Physics from the University of Wisconsin, as well as a PhD in Computer Science and Bioinformatics from University of California, Santa Cruz.

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  • Informatics and Machine Learning: From Martingales to Metaheuristics
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