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This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks.    The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of arti

Evolutionary Machine Learning Techniques

ENbook.pl

pstrongDESCRIPTION strongppThe book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning ML algorithms and the practical implementation of the various ML approaches.pp ppThis book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Nave Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process MDP, Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action SARSA. It also includes various feature extraction and 

Machine Learning | Dr Kamal Kant Hiran Ruchi Doshi | Twarda

This book presents recent advancements in research, new methods and techniques, and applications in decision support systems DSS with Machine Learning and Probabilistic Graphical Models. The book undertakes to stimulate scientific exchange, ideas, and experiences in the field of DSS applications.

Machine Learning and Probabilistic Graphical Models for Decision Support Systems | Tran Kim Phuc | Paperback | Twarda

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix no

Foundations of Data Science | Blum Avrim | Twarda | Twarda

This compendium brings together the fields of Quantum Computing, Machine Learning, and Neuromorphic Computing. It provides an elementary introduction for students and researchers interested in quantum or neuromorphic computing to the basics of machine learning and the possibilities for using quantum devices for pattern recognition and Bayesian decision tree problems. The volume also highlights some possibly new insights into the meaning of quantum mechanics, for example, why a description of Nature requires probabilistic rather than deterministic methods.

Quantum Mechanics and Bayesian Machines | Chapline George | Twarda | Twarda

A new edition of this book is also available on Amazon. Imagine what an astonishing effect could happen if we combined profound human natural creativity with computers' numerical and logical capabilities. Probably, individuals that will use this technology first will immensely benefit from it. brMany impressive achievements have recently emerged in machine learning and narrow AI. For example, modern deep neural networks can recognize and even generate some types of patterns even better than humans can. Some experts think that deep learning is a direct road to artificial general intelligence, but others think differently and propose alternative ideas such as evolutionary computations, cellular automata, probabilistic programming, or quantum computing. brThis book will cover many approaches to machine learning and the main classes of artificial neural networks and how these computational approaches can potentially lead to the creation of artificial general intelligence.brAlso, you will f

How to Create Machine Superintelligence | Kovera Artem | Twarda

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overv

The Probability Companion for Engineering and Computer Science | Prgel | Bennett Adam | Twarda | Twarda

pbProfessor Judea Pearl won the 2011 Turing Award for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.b This book contains the original articles that led to the award, as well as other seminal works, divided into four parts heuristic search, probabilistic reasoning, causality, first period 1988-2001, and causality, recent period 2002-2020. Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl's work in different fields from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.p

Probabilistic and Causal Inference | Geffner Hector | Twarda

pThe proclivity of today's technology to think like humans may be seen in new developing disciplines such as neural computing, fuzzy logic, evolutionary computation, machine learning, and probabilistic reasoning. These strategies are grouped together into one main technique known as soft computing. This book discusses the most recent soft computing and fuzzy logic-based applications and innovations in industrial advancements, supply chain and logistics, system optimization, decision-making, artificial intelligence, smart systems, and other rapidly evolving technologies. In today's competitive world, the book provides soft computing solutions to help companies overcome the obstacles posed by sophisticated decision-making systems.p

Advances in Soft Computing Applications | Kharola Shristi | Twarda | Twarda

This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that o

Prediction, Learning, and Games | Cesa | Bianchi Nicolo | Twarda | Twarda

pRemote sensing plays a pivotal role in understanding where and how floods and glacier geohazards occur their severity, causes and types and the risk that they may pose to populations, activities and properties. By providing a spectrum of imaging capabilities, resolutions and temporal and spatial coverage, remote sensing data acquired from satellite, aerial and ground-based platforms provide key geo-information to characterize and model these processes. This book includes research papers on novel technologies e.g., sensors, platforms, data e.g., multi-spectral, radar, laser scanning, GPS, gravity and analysis methods e.g., change detection, offset tracking, structure from motion, 3D modeling, radar interferometry, automated classification, machine learning, spectral indices, probabilistic approaches for flood and glacier imaging. Through target applications and case studies distributed globally, these articles contribute to the discussion on the current potential and limitations of rem

Imaging Floods and Glacier Geohazards with Remote Sensing | Cigna Francesca | Twarda | Twarda

Introduction.- Single-carrier advanced modulation formats.- Basic Digital signal processing for single carrier signals.- Quasi-linear coherent optical transmission system and digital signal processing.- Super Nyquist wavelength division multiplexing system.- All-optical Nyquist signal.- Nonlinear compensation in optical fiber.- Probabilistic Shaping.- High baud signal transmission.- Advanced modulation code optical signal transmission technology.- Carrierless amplitude and phase modulation.- PAM 4 signal modulation and digital signal processing-based detection technology.- Optical OFDM.- Direct Detection OFDM.- Intensity Modulation Direct Detection High Speed Fiber Access System.- High-speed fiber access system based on direct detection of IQ modulation.- Forward Error Correction.- High Spectral Efficiency Optical Four-Dimensional Modulation.- Machine Learning Algorithm in the Optical Communication System.- Kramers-Kronig receiver in direct detection.

Digital Signal Processing in High | Speed Optical Fiber Communication Principle and Application | Yu Jianjun | Paperback | Twarda

Concentration inequalities for functions of independent random variables is an area of probability theory that has witnessed a great revolution in the last few decades, and has applications in a wide variety of areas such as machine learning, statistics, discrete mathematics, and high-dimensional geometry. Roughly speaking, if a function of many independent random variables does not depend too much on any of the variables then it is concentrated in the sense that with high probability, it is close to its expected value. This book offers a host of inequalities to illustrate this rich theory in an accessible way by covering the key developments and applications in the field. brThe authors describe the interplay between the probabilistic structure independence and a variety of tools ranging from functional inequalities to transportation arguments to information theory. Applications to the study of empirical processes, random projections, random matrix theory, and threshold phenomena are a

Concentration Inequalities | Boucheron Stephane | Twarda

pChapter 1 Introduction.- Chapter 2 History of Boko Haram.- Chapter 3 Temporal Probabilistic Rules and Policy Computation Algorithms.- Chapter 4 Sexual Violence.- Chapter 5 Suicide Bombings.- Chapter 6 Abductions.- Chapter 7 Arson.- Chapter 8 Other Types of Attacks.- Appendix A All TP-Rules.- Appendix B Data Collection.- Appendix C Most Used Variables.- Appendix D Sample Boko Haram Report.p

A Machine Learning Based Model of Boko Haram | Subrahmanian V. S. | Paperback | Twarda

This book covers probability and statistics from the machine learning perspective. From probability to machine learning Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner.

Probability and Statistics for Machine Learning | Aggarwal Charu C. | Twarda

1 Introduction.- 2 Model Selection and Evaluation.- 3 Linear Models.- 4 Decision Trees.- 5 Neural Networks.- 6 Support Vector Machine.- 7 Bayes Classifiers.- 8 Ensemble Learning.- 9 Clustering.- 10 Dimensionality Reduction and Metric Learning.- 11 Feature Selection and Sparse Learning.- 12 Computational Learning Theory.- 13 Semi-Supervised Learning.- 14 Probabilistic Graphical Models.- 15 Rule Learning.- 16 Reinforcement Learning.

Machine Learning | Zhou Zhi | Hua | Paperback | Twarda

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pHow can music withstand the death and destruction brought on by war Global conflicts of the 20th century fundamentally transformed not only national boundaries, power relations, and global economies, but also the arts and culture of every nation involved. An important, unacknowledged aspect of these conflicts is that they have unique musical soundtracks. iMusic in World War IIi explores how music and sound took on radically different dimensions in the United States and Europe before, during, and after World War II. Additionally, the collection examines the impact of radio and film as the disseminators of the war's musical soundtrack. Contributors contend that the European and American soundtrack of World War II was largely one of escapism rather than the lofty, solemn, heroic, and celebratory mode of war music in the past. Furthermore, they explore the variety of experiences of populations forced from their homes and interned in civilian and POW camps in Europe and the United States, 

Music in World War II | Potter Pamela M. | Twarda

ENdisc.pl

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