Neuromanifolds [113–115]
8.5 Exercises
14 9 Neural Net Learning and Loss Bounds Analysis 9.1 Brief Introduction to Neural Nets (NNs) 9.2 Variational Learning Formalism and Use in Loss Bounds Analysis 9.3 The “sinh−1(ω)” link algorithm (SA) 9.4 The Loss Bounds Analysis for sinh−1(ω) 9.5 Exercises
15 10 Classification and Clustering 10.1 The SVM Classifier – An Overview 10.2 Introduction to Classification and Clustering 10.3 Lagrangian Optimization and Structural Risk Minimization (SRM) 10.4 SVM Binary Classifier Implementation 10.5 Kernel Selection and Tuning Metaheuristics 10.6 SVM Multiclass from Decision Tree with SVM Binary Classifiers 10.7 SVM Multiclass Classifier Derivation (Multiple Decision Surface) 10.8 SVM Clustering 10.9 Exercises
16 11 Search Metaheuristics 11.1 Trajectory‐Based Search Metaheuristics 11.2 Population‐Based Search Metaheuristics 11.3 Exercises
17 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 12.3 Channel Current Cheminformatics (CCC) Implementation of the Stochastic Sequential Analysis (SSA) Protocol 12.4 SCW for Detector Sensitivity Boosting 12.5 SSA for Deep Learning 12.6 Exercises
18 13 Deep Learning Tools – TensorFlow 13.1 Neural Nets Review 13.2 TensorFlow from Google 13.3 Exercises
19 14 Nanopore Detection – A Case Study 14.1 Standard Apparatus 14.2 Controlling Nanopore Noise Sources and Choice of Aperture 14.3 Length Resolution of Individual DNA Hairpins 14.4 Detection of Single Nucleotide Differences (Large Changes in Structure) 14.5 Blockade Mechanism for 9bphp 14.6 Conformational Kinetics on Model Biomolecules 14.7 Channel Current Cheminformatics 14.8 Channel‐Based Detection Mechanisms 14.9 The NTD Nanoscope 14.10 NTD Biosensing Methods 14.11 Exercises
20 Appendix A: Python and Perl System Programming in Linux A.1 Getting Linux and Python in a Flash (Drive) A.2 Linux and the Command Shell A.3 Perl Review: I/O, Primitives, String Handling, Regex
21 Appendix B: Physics B.1 The Calculus of Variations
22 Appendix C: Math C.1 Martingales [102] C.2 Hoeffding Inequality
23 References
24 Index
List of Tables
1 Chapter 3Table 3.1 (tag) Gap sizes, with bin size 100.Table 3.2 (aaa) Gap sizes, with bin size 100.
2 Chapter 5Table 5.1 High frequency word counts from Il principle.Table 5.2 Keyword types (I power; II opportunity; III parties; IV actions).Table 5.3 The three highest frequency words.Table 5.4 The proximate high frequency wordsTable 5.5 High frequency up to first word that is not subjunctive+ or romant...Table 5.6 High frequency words given in terms of six categories: heart, powe...Table 5.7 Sample sentiment table values.Table 5.8 Shakespeare insult kit (internet author anonymous).
3 Chapter 10Table 10.1 Performance comparison table for the different SVM methods.Table 10.2 Sequential chunking using different DNA hairpin datasets.Table 10.3 Multi‐threaded chunking using different DNA hairpin datasetsTable 10.4 Sequential chunking with the Absdiff kernel.Table 10.5 Multi‐threaded chunking with the Absdiff kernel.Table 10.6 Performance comparison
