Machine Learning Algorithms for Problem Solving in Computational Applications

1.

Machine Learning Algorithms for Problem Solving in Computational Applications—Intelligent Techniques

出版日：2012/06/30 作者：Siddhivinayak Kulkarni (EDT) 出版社：Igi Global 裝訂：精裝

The 21 papers in this collection develop new algorithms for helping machines learn from previous data and describe the application of machine learning algorithms to image retrieval, computer vision, h

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Understanding Machine Learning ― From Theory to Algorithms

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2.

Understanding Machine Learning ― From Theory to Algorithms

出版日：2014/05/31 作者：Shai Shalev-Shwartz 出版社：Cambridge Univ Pr 裝訂：精裝

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible t

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3.

Machine Learning In Bioinformatics

出版日：2008/11/13 作者：Zhang 出版社：John Wiley & Sons Inc 裝訂：精裝

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology a

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Data Mining and Machine Learning ― Fundamental Concepts and Algorithms

90 折

4.

Data Mining and Machine Learning ― Fundamental Concepts and Algorithms

出版日：2020/02/29 作者：Mohammed J. Zaki 出版社：Cambridge Univ Pr 裝訂：精裝

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

優惠價： 9 3288

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5.

Simulation And Computational Red Teaming For Problem Solving

出版日：2019/11/13 作者：Tang 出版社：John Wiley & Sons Inc 裝訂：精裝

An authoritative guide to computer simulation grounded in a multi-disciplinary approach for solving complex problems Simulation and Computational Red Teaming for Problem Solving offers a review of com

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6.

Computer Vision ─ Principles, Algorithms, Applications, Learning

出版日：2017/10/20 作者：E. R. Davies 出版社：Academic Pr 裝訂：精裝

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering

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7.

Machine Learning Paradigms ― Applications in Recommender Systems

出版日：2015/06/25 作者：Aristomenis S. Lampropoulos; George A. Tsihrintzis 出版社：Springer Verlag 裝訂：精裝

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender

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8.

Kernel Methods and Machine Learning

出版日：2014/04/30 作者：S. Y. Kung 出版社：Cambridge Univ Pr 裝訂：精裝

Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate student

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9.

Quotient Space Based Problem Solving ― A Theoretical Foundation of Granular Computing

出版日：2014/02/27 作者：Ling Zhang; Bo Zhang 出版社：Elsevier Science Ltd 裝訂：精裝

Quotient Space Based Problem Solving provides an in-depth treatment of hierarchical problem solving, computational complexity, and the principles and applications of multi-granular computing, includin

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10.

Computer Algorithms ― Introduction to Design and Analysis

出版日：1999/11/01 作者：Sara Baase; Allen Van Gelder 出版社：Pearson College Div 裝訂：精裝

This college textbook introduces algorithms for solving real problems that arise frequently in computer applications, basic principles of computational complexity, and NP -completeness and parallel a

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Deep Learning Neural Networks ─ Design and Case Studies

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11.

Deep Learning Neural Networks ─ Design and Case Studies

出版日：2016/08/02 作者：Daniel Graupe 出版社：World Scientific Pub Co Inc 裝訂：精裝

Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems

優惠價： 9 2693

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12.

Artificial Communication

出版日：2022/04/05 作者：Elena Esposito 出版社：Mit Pr 裝訂：精裝

A proposal that we think about digital technologies such as machine learning not in terms of artificial intelligence but as artificial communication. Algorithms that work with deep learning and big data are getting so much better at doing so many things that it makes us uncomfortable. How can a device know what our favorite songs are, or what we should write in an email? Have machines become too smart? In Artificial Communication, Elena Esposito argues that drawing this sort of analogy between algorithms and human intelligence is misleading. If machines contribute to social intelligence, it will not be because they have learned how to think like us but because we have learned how to communicate with them. Esposito proposes that we think of “smart” machines not in terms of artificial intelligence but as artificial communication. To do this, we need a concept of communication that can take into account the possibility that a communication partner may not be a human being but an algor

優惠:外文書周末優惠-單79雙75 優惠價： 79 839

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13.

Algorithms for Decision Making

出版日：2022/08/02 作者：Mykel J. Kochenderfer 出版社：Mit Pr 裝訂：精裝

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.Automated decision-making systems or decision-support systems―used in applications that range from aircraft collision avoidance to breast cancer screening―must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through inte

優惠:外文書周末優惠-單79雙75 優惠價： 79 4503

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Introduction to Algorithms, fourth edition

79 折

14.

Introduction to Algorithms, fourth edition

出版日：2022/03/22 作者：Thomas H. Cormen 出版社：Mit Pr 裝訂：精裝

A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learningNew material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays140 new exerc

優惠:外文書周末優惠-單79雙75 優惠價： 79 7110

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Beyond the Worst-Case Analysis of Algorithms

90 折

15.

Beyond the Worst-Case Analysis of Algorithms

出版日：2020/09/30 作者：Tim Roughgarden 出版社：Cambridge Univ Pr 裝訂：精裝

There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.

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Machine Learning Refined ― Foundations, Algorithms, and Applications

90 折

16.

Machine Learning Refined ― Foundations, Algorithms, and Applications

出版日：2020/02/29 作者：Jeremy Watt 出版社：Cambridge Univ Pr 裝訂：精裝

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad

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17.

Machine Learning ─ Algorithms and Applications

出版日：2016/08/03 作者：Mohssen Mohammed; Muhammad Badruddin Khan; Ejhab Bashier Mohammed Bashier 出版社：Productivity Press 裝訂：精裝

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newc

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18.

Machine Learning Models and Algorithms for Big Data Classification ― Thinking With Examples for Effective Learning

出版日：2015/10/21 作者：Shan Suthaharan 出版社：Springer Verlag 裝訂：精裝

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random for

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19.

Introduction to Machine Learning

出版日：2014/08/22 作者：Ethem Alpaydin 出版社：Mit Pr 裝訂：精裝

The goal of machine learning is to program computers to use example data or pastexperience to solve a given problem. Many successful applications of machine learning exist already,including systems th

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20.

Introduction to Machine Learning

出版日：2009/12/04 作者：Ethem Alpaydin 出版社：Mit Pr 裝訂：精裝

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

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21.

Introduction To Machine Learning

出版日：2004/10/15 作者：Ethem Alpaydin 出版社：Mit Pr 裝訂：精裝

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

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22.

Introduction to Genetic Algorithms

出版日：1996/02/27 作者：Melanie Mitchell 出版社：Bradford Books 裝訂：精裝

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible i

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The Finite Element Method ─ Basic Concepts and Applications With MATLAB, MAPLE, and COMSOL

75 折

23.

The Finite Element Method ─ Basic Concepts and Applications With MATLAB, MAPLE, and COMSOL

出版日：2017/04/05 作者：Darrell W. Pepper; Juan C. Heinrich 出版社：Productivity Press 裝訂：精裝

The third edition of the book introduces the fundamentals of the finite element method through simple examples and an applications-oriented approach using the latest computational tools. Using the tra

優惠:Taylor and Francis 書展優惠價： 75 6525

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66 折

24.

Applied Combinatorics, Sixth Edition

出版日：2012/01/09 作者：Tucker 出版社：John Wiley & Sons Inc 裝訂：精裝

The new 6th edition of Applied Combinatorics builds on the previous editions with more in depth analysis of computer systems in order to help develop proficiency in basic discrete math problem solving

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25.

Machine Learning from Weak Supervision

出版日：2022/08/23 作者：Masashi Sugiyama 出版社：Mit Pr 裝訂：精裝

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif

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Probabilistic Numerics：Computation as Machine Learning

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26.

Probabilistic Numerics：Computation as Machine Learning

出版日：2022/06/30 作者：Philipp Hennig 出版社：Cambridge Univ Pr 裝訂：精裝

Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.

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27.

Handbook of Machine Learning ― Optimization and Decision Making

出版日：2019/10/29 作者：Collins Achepsah Leke; Tshilidzi Marwala 出版社：World Scientific Publishing Co Pte Ltd 裝訂：精裝

Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like th

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28.

Industrial Applications of Machine Learning

出版日：2018/12/10 作者：Pedro Larrañaga; Alberto Ogbechie; Javier Diaz-rozo; David Atienza; Concha Bielza 出版社：CRC Pr I Llc 裝訂：精裝

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools

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29.

Applications of Metaheuristic Optimization Algorithms in Civil Engineering

出版日：2016/12/16 作者：A. Kaveh 出版社：Springer Verlag 裝訂：精裝

The book presents recently developed efficient meta-heuristic optimization algorithms and their applications for solving various optimization problems in civil engineering. The concepts can also be us

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30.

Machine Learning and Medical Imaging

出版日：2016/08/19 作者：Guorong Wu; Dinggang Shen 出版社：Academic Pr 裝訂：精裝

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, inc

優惠價： 1 4338

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31.

Computational Trust Models and Machine Learning

出版日：2014/10/17 作者：Xin Liu (EDT); Anwitaman Datta (EDT); Ee-Peng Lim (EDT) 出版社：Taylor & Francis 裝訂：精裝

"This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying form

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32.

Tractability

出版日：2014/02/28 作者：Lucas Bordeaux 出版社：Cambridge Univ Pr 裝訂：精裝

Classical computer science textbooks tell us that some problems are 'hard'. Yet many areas, from machine learning and computer vision to theorem proving and software verification, have defined their own set of tools for effectively solving complex problems. Tractability provides an overview of these different techniques, and of the fundamental concepts and properties used to tame intractability. This book will help you understand what to do when facing a hard computational problem. Can the problem be modelled by convex, or submodular functions? Will the instances arising in practice be of low treewidth, or exhibit another specific graph structure that makes them easy? Is it acceptable to use scalable, but approximate algorithms? A wide range of approaches is presented through self-contained chapters written by authoritative researchers on each topic. As a reference on a core problem in computer science, this book will appeal to theoreticians and practitioners alike.

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33.

Problem Solving In Enzyme Biocatalysis

出版日：2013/10/17 作者：Illanes 出版社：John Wiley & Sons Inc 裝訂：精裝

"The only book to approach enzyme kinetics with a problem-solving focus, for practical applications in the food, pharmaceutical, and fine chemistry industry"--

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34.

Automated Design of Analog and High-frequency Circuits: ― A Computational Intelligence Approach

出版日：2013/08/26 作者：Bo Liu; Georges Gielen; Francisco V. Fern憳ez 出版社：Springer Verlag 裝訂：精裝

Computational intelligence techniques are becoming more and more important for automated problem solving nowadays. Due to the growing complexity of industrial applications and the increasingly tight t

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35.

System and Technology Advancements in Distance Learning

出版日：2012/08/31 作者：Vive Kumar; Fuhua Lin 出版社：Igi Global 裝訂：精裝

"This book reviews distance learning programs and software systems, outlining computational methods, algorithms, implemented prototype systems, and applications of open and distance learning"--Provide

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36.

Multi-label Dimensionality Reduction

出版日：2011/08/15 作者：Jieping Ye; Shuiwang Ji; Liang Sun 出版社：Chapman & Hall 裝訂：精裝

A comprehensive reference for researchers in machine learning, data mining, and computer vision, this book presents in-depth, systematic discussions on algorithms and applications for dimensionality r

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37.

Combinatorial Machine Learning

出版日：2011/07/01 作者：Mikhail Moshkov; Beata Zielosko 出版社：Springer-Verlag New York Inc 裝訂：精裝

Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Reducts play key role in t

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38.

Problem Solving for Better Health: A Global Perspective

出版日：2010/11/22 作者：Barry H. Smith M.D. Ph.D. (EDT); Joyce J. Fitzpatrick Ph.D. (EDT); Pamela Hoyt-Hudson (EDT) 出版社：Springer Pub Co 裝訂：精裝

This book describes numerous applications of the the Dryfus Health Foundation's Problem-Solving for Better Health Program, and related programs, featuring problem solving models used in communities,

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39.

Machine Learning for Spatial Environmental Data ─ Theory, Applications and Software

出版日：2009/06/09 作者：Mikhail Kanevski; Alexei Pozdnoukhov; Vadim Timonin 出版社：CRC Press UK 裝訂：精裝

This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and

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40.

Computational Learning & Probabilistic Reasoning

出版日：1996/05/28 作者：Gammerman 出版社：John Wiley & Sons Inc 裝訂：精裝

Providing a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intellig

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Machine Learning Algorithms for Problem Solving in Computational Applications