Informatics and machine learning from Martingales to metaheuristics

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 d...

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Bibliographic Details
Main Author Winters-Hilt, Stephen
Format eBook Book
LanguageEnglish
Published Hoboken, N.J Wiley 2022
John Wiley & Sons, Incorporated
Wiley-Blackwell
Edition1
Subjects
Bioinformatics
Computer science
Machine learning
Online AccessGet full text
ISBN1119716578
9781119716570
1119716748
9781119716747
DOI10.1002/9781119716730

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Table of Contents:
  • 3.2 Codon Discovery from Mutual Information Anomaly -- 3.3 ORF Discovery from Long-Tail Distribution Anomaly -- 3.3.1 Ab initio Learning with smORF´s, Holistic Modeling, and Bootstrap Learning -- 3.4 Sequential Processes and Markov Models -- 3.4.1 Markov Chains -- 3.5 Exercises -- Chapter 4 Ad Hoc, Ab Initio, and Bootstrap Signal Acquisition Methods -- 4.1 Signal Acquisition, or Scanning, at Linear Order Time-Complexity -- 4.2 Genome Analytics: The Gene-Finder -- 4.3 Objective Performance Evaluation: Sensitivity and Specificity -- 4.4 Signal Analytics: The Time-Domain Finite State Automaton (tFSA) -- 4.4.1 tFSA Spike Detector -- 4.4.2 tFSA-Based Channel Signal Acquisition Methods with Stable Baseline -- 4.4.3 tFSA-Based Channel Signal Acquisition Methods Without Stable Baseline -- 4.5 Signal Statistics (Fast): Mean, Variance, and Boxcar Filter -- 4.5.1 Efficient Implementations for Statistical Tools (O(L)) -- 4.6 Signal Spectrum: Nyquist Criterion, Gabor Limit, Power Spectrum -- 4.6.1 Nyquist Sampling Theorem -- 4.6.2 Fourier Transforms, and Other Classic Transforms -- 4.6.3 Power Spectral Density -- 4.6.4 Power-Spectrum-Based Feature Extraction -- 4.6.5 Cross-Power Spectral Density -- 4.6.6 AM/FM/PM Communications Protocol -- 4.7 Exercises -- Chapter 5 Text Analytics -- 5.1 Words -- 5.1.1 Text Acquisition: Text Scraping and Associative Memory -- 5.1.2 Word Frequency Analysis: Machiavelli´s Polysemy on Fortuna and Virtu -- 5.1.3 Word Frequency Analysis: Coleridge´s Hidden Polysemy on Logos -- 5.1.4 Sentiment Analysis -- 5.2 Phrases-Short (Three Words) -- 5.2.1 Shakespearean Insult Generation-Phrase Generation -- 5.3 Phrases-Long (A Line or Sentence) -- 5.3.1 Iambic Phrase Analysis: Shakespeare -- 5.3.2 Natural Language Processing -- 5.3.3 Sentence and Story Generation: Tarot -- 5.4 Exercises -- Chapter 6 Analysis of Sequential Data Using HMMs
  • 6.1 Hidden Markov Models (HMMs) -- 6.1.1 Background and Role in Stochastic Sequential Analysis (SSA) -- 6.1.2 When to Use a Hidden Markov Model (HMM)? -- 6.1.3 Hidden Markov Models (HMMs)-Standard Formulation and Terms -- 6.2 Graphical Models for Markov Models and Hidden Markov Models -- 6.2.1 Hidden Markov Models -- 6.2.2 Viterbi Path -- 6.2.3 Forward and Backward Probabilities -- 6.2.4 HMM: Maximum Likelihood discrimination -- 6.2.5 Expectation/Maximization (Baum-Welch) -- 6.3 Standard HMM Weaknesses and their GHMM Fixes -- 6.4 Generalized HMMs (GHMMs - "Gems"): Minor Viterbi Variants -- 6.4.1 The Generic HMM -- 6.4.2 pMM/SVM -- 6.4.3 EM and Feature Extraction via EVA Projection -- 6.4.4 Feature Extraction via Data Absorption (a.k.a. Emission Inversion) -- 6.4.5 Modified AdaBoost for Feature Selection and Data Fusion -- 6.5 HMM Implementation for Viterbi (in C and Perl) -- 6.6 Exercises -- Chapter 7 Generalized HMMs (GHMMs): Major Viterbi Variants -- 7.1 GHMMs: Maximal Clique for Viterbi and Baum-Welch -- 7.2 GHMMs: Full Duration Model -- 7.2.1 HMM with Duration (HMMD) -- 7.2.2 Hidden Semi-Markov Models (HSMM) with sid-information -- 7.2.3 HMM with Binned Duration (HMMBD) -- 7.3 GHMMs: Linear Memory Baum-Welch Algorithm -- 7.4 GHMMs: Distributable Viterbi and Baum-Welch Algorithms -- 7.4.1 Distributed HMM processing via "Viterbi-overlap-chunking" with GPU speedup -- 7.4.2 Relative Entropy and Viterbi Scoring -- 7.5 Martingales and the Feasibility of Statistical Learning (further details in Appendix) -- 7.6 Exercises -- Chapter 8 Neuromanifolds and the Uniqueness of Relative Entropy -- 8.1 Overview -- 8.2 Review of Differential Geometry -- 8.2.1 Differential Topology - Natural Manifold -- 8.2.2 Differential Geometry - Natural Geometric Structures -- 8.3 Amari´s Dually Flat Formulation -- 8.3.1 Generalization of Pythagorean Theorem
  • Cover -- Title Page -- Copyright Page -- Contents -- Chapter 1 Introduction -- 1.1 Data Science: Statistics, Probability, Calculus Python (or Perl) and Linux -- 1.2 Informatics and Data Analytics -- 1.3 FSA-Based Signal Acquisition and Bioinformatics -- 1.4 Feature Extraction and Language Analytics -- 1.5 Feature Extraction and Gene Structure Identification -- 1.5.1 HMMs for Analysis of Information Encoding Molecules -- 1.5.2 HMMs for Cheminformatics and Generic Signal Analysis -- 1.6 Theoretical Foundations for Learning -- 1.7 Classification and Clustering -- 1.8 Search -- 1.9 Stochastic Sequential Analysis (SSA) Protocol (Deep Learning Without NNs) -- 1.9.1 Stochastic Carrier Wave (SCW) Analysis-Nanoscope Signal Analysis -- 1.9.2 Nanoscope Cheminformatics-A Case Study for Device ``Smartening´´ -- 1.10 Deep Learning using Neural Nets -- 1.11 Mathematical Specifics and Computational Implementations -- Chapter 2 Probabilistic Reasoning and Bioinformatics -- 2.1 Python Shell Scripting -- 2.1.1 Sample Size Complications -- 2.2 Counting, the Enumeration Problem, and Statistics -- 2.3 From Counts to Frequencies to Probabilities -- 2.4 Identifying Emergent/Convergent Statistics and Anomalous Statistics -- 2.5 Statistics, Conditional Probability, and Bayes' Rule -- 2.5.1 The Calculus of Conditional Probabilities: The Cox Derivation -- 2.5.2 Bayes' Rule -- 2.5.3 Estimation Based on Maximal Conditional Probabilities -- 2.6 Emergent Distributions and Series -- 2.6.1 The Law of Large Numbers (LLN) -- 2.6.2 Distributions -- 2.6.3 Series -- 2.7 Exercises -- Chapter 3 Information Entropy and Statistical Measures -- 3.1 Shannon Entropy, Relative Entropy, Maxent, Mutual Information -- 3.1.1 The Khinchin Derivation -- 3.1.2 Maximum Entropy Principle -- 3.1.3 Relative Entropy and Its Uniqueness -- 3.1.4 Mutual Information -- 3.1.5 Information Measures Recap
  • 10.4.3 Chunking on Large Datasets: O(N2) ➔ n O(N2/n2) = O(N2)/n -- 10.4.4 Support Vector Reduction (SVR) -- 10.4.5 Code Examples (in OO Perl) -- 10.5 Kernel Selection and Tuning Metaheuristics -- 10.5.1 The ``Stability´´ Kernels -- 10.5.2 Derivation of ``Stability´´ Kernels -- 10.5.3 Entropic and Gaussian Kernels Relate to Unique, Minimally Structured, Information Divergence and Geometric Distance ... -- 10.5.4 Automated Kernel Selection and Tuning -- 10.6 SVM Multiclass from Decision Tree with SVM Binary Classifiers -- 10.7 SVM Multiclass Classifier Derivation (Multiple Decision Surface) -- 10.7.1 Decomposition Method to Solve the Dual -- 10.7.2 SVM Speedup via Differentiating BSVs and SVs -- 10.8 SVM Clustering -- 10.8.1 SVM-External Clustering -- 10.8.2 Single-Convergence SVM-Clustering: Comparative Analysis -- 10.8.3 Stabilized, Single-Convergence Initialized, SVM-External Clustering -- 10.8.4 Stabilized, Multiple-Convergence, SVM-External Clustering -- 10.8.5 SVM-External Clustering-Algorithmic Variants -- 10.9 Exercises -- Chapter 11 Search Metaheuristics -- 11.1 Trajectory-Based Search Metaheuristics -- 11.1.1 Optimal-Fitness Configuration Trajectories - Fitness Function Known and Sufficiently Regular -- 11.1.2 Optimal-Fitness Configuration Trajectories - Fitness Function not Known -- 11.1.3 Fitness Configuration Trajectories with Nonoptimal Updates -- 11.2 Population-Based Search Metaheuristics -- 11.2.1 Population with Evolution -- 11.2.2 Population with Group Interaction - Swarm Intelligence -- 11.2.3 Population with Indirect Interaction via Artifact -- 11.3 Exercises -- Chapter 12 Stochastic Sequential Analysis (SSA) -- 12.1 HMM and FSA-Based Methods for Signal Acquisition and Feature Extraction -- 12.2 The Stochastic Sequential Analysis (SSA) Protocol -- 12.2.1 (Stage 1) Primitive Feature Identification
  • 8.3.2 Projection Theorem and Relation Between Divergence and Link Formalism -- 8.4 Neuromanifolds -- 8.5 Exercises -- Chapter 9 Neural Net Learning and Loss Bounds Analysis -- 9.1 Brief Introduction to Neural Nets (NNs) -- 9.1.1 Single Neuron Discriminator -- 9.1.2 Neural Net with Back-Propagation -- 9.2 Variational Learning Formalism and Use in Loss Bounds Analysis -- 9.2.1 Variational Basis for Update Rule -- 9.2.2 Review and Generalization of GD Loss Bounds Analysis -- 9.2.3 Review of the EG Loss Bounds Analysis -- 9.3 The The "sinh−1(ω)" link algorithm (SA) -- 9.3.1 Motivation for "sinh−1(ω)" link algorithm (SA) -- 9.3.2 Relation of sinh Link Algorithm to the Binary Exponentiated Gradient Algorithm -- 9.4 The Loss Bounds Analysis for sinh−1(ω) -- 9.4.1 Loss Bounds Analysis Using the Taylor Series Approach -- 9.4.2 Loss Bounds Analysis Using Taylor Series for the sinh Link (SA) Algorithm -- 9.5 Exercises -- Chapter 10 Classification and Clustering -- 10.1 The SVM Classifier-An Overview -- 10.2 Introduction to Classification and Clustering -- 10.2.1 Sum of Squared Error (SSE) Scoring -- 10.2.2 K-Means Clustering (Unsupervised Learning) -- 10.2.3 k-Nearest Neighbors Classification (Supervised Learning) -- 10.2.4 The Perceptron Recap (See Chapter for Details) -- 10.3 Lagrangian Optimization and Structural Risk Minimization (SRM) -- 10.3.1 Decision Boundary and SRM Construction Using Lagrangian -- 10.3.2 The Theory of Classification -- 10.3.3 The Mathematics of the Feasibility of Learning -- 10.3.4 Lagrangian Optimization -- 10.3.5 The Support Vector Machine (SVM)-Lagrangian with SRM -- 10.3.6 Kernel Construction Using Polarization -- 10.3.7 SVM Binary Classifier Derivation -- 10.4 SVM Binary Classifier Implementation -- 10.4.1 Sequential Minimal Optimization (SMO) -- 10.4.2 Alpha-Selection Variants
  • 12.2.2 (Stage 2) Feature Identification and Feature Selection