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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180 Days of Problem Solving for Kindergarten

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3.

180 Days of Problem Solving for Kindergarten

出版日：2016/10/03 作者：Jessica Hathaway 出版社：Shell Education 裝訂：平裝

Take your students on a year-round problem-solving adventure with thematic units that hone in on the key skills needed to meet today's standards. With the changes in mathematics standards, teachers ne

優惠價： 85 743

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4.

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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180 Days of Problem Solving for Third Grade

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5.

180 Days of Problem Solving for Third Grade

出版日：2016/10/03 作者：Kristin Kemp 出版社：Shell Education 裝訂：平裝

Take your students on a year-round problem-solving adventure with thematic units that hone in on the key skills needed to meet today's standards. With the changes in mathematics standards, teachers ne

優惠價： 85 743

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180 Days of Problem Solving for Fourth Grade

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6.

180 Days of Problem Solving for Fourth Grade

出版日：2016/10/03 作者：Chuck Aracich 出版社：Shell Education 裝訂：平裝

Take your students on a year-round problem-solving adventure with thematic units that hone in on the key skills needed to meet today's standards. With the changes in mathematics standards, teachers ne

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Unsupervised Machine Learning for Clustering in Political and Social Research

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7.

Unsupervised Machine Learning for Clustering in Political and Social Research

出版日：2020/09/30 作者：Philip D. Waggoner 出版社：Cambridge Univ Pr 裝訂：平裝

In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.

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Data Mining and Machine Learning ― Fundamental Concepts and Algorithms

90 折

8.

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.

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9.

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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10.

Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology

出版日：2018/01/03 作者：eculai Andrei 出版社：Springer Verlag 裝訂：平裝

This book presents the theoretical details and computational performances of algorithms used for solving continuous nonlinear optimization applications imbedded in GAMS. Aimed toward scientists and gr

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11.

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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12.

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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13.

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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14.

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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MACHINE VISION ALGORITHMS AND APPLICATIONS

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15.

MACHINE VISION ALGORITHMS AND APPLICATIONS

出版日：2007/11/14 作者：STEGER 出版社：JOHN WILEY & SONS;LTD 裝訂：平裝

Machine Vision Algorithms and Applications is the first up-to-date textbook for machine vision software provides all the details on the theory and practical use of the relevant algorithms. The first

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16.

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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English for Everyone Course Book Level 3 Intermediate (with Online Audio)(平裝本)(美國版)

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17.

English for Everyone Course Book Level 3 Intermediate (with Online Audio)(平裝本)(美國版)

出版日：2025/06/17 作者：DK 出版社：DK US 裝訂：平裝

Take your self-study English language learning a step further with the third book in the easy-to-use, visual learning series.English for Everyone Level 3 Intermediate Course Book introduces topics such as telling a story, describing new experiences, relationships, and problem solving. The step-by-step teaching methods are clear and engaging, making the English language easy to learn by yourself. Key language skills, grammar rules, and vocabulary are explained alongside colorful illustrations to help you put new language into context. Listening, speaking, reading, and writing exercises are set in bite-sized modules, allowing you to learn at your own pace.Free audio is available online to support your language learning, featuring native English speakers. The audio examples help you to build experience of spoken English and give you the chance to perfect your pronunciation.English for Everyone is aligned to the CEFR, the international standard for language learning, and ideal for preparat

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18.

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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DATA STRUCTURES AND ALGORITHMS IN JAVA 5/E INTERNATIONAL STUDENT VERSION

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19.

DATA STRUCTURES AND ALGORITHMS IN JAVA 5/E INTERNATIONAL STUDENT VERSION

出版日：2010/03/16 作者：GOODRICH 出版社：JOHN WILEY & SONS;LTD 裝訂：平裝

Using a unique multimedia format for learning the fundamentals of data structures and algorithms, this conceptually elegant and innovative text incorporates the object-oriented design paradigm with Ja

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Practical Machine Learning and Image Processing ― For Facial Recognition, Object Detection, and Pattern Recognition Using Python

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20.

Practical Machine Learning and Image Processing ― For Facial Recognition, Object Detection, and Pattern Recognition Using Python

出版日：2019/03/01 作者：Himanshu Singh 出版社：Apress 裝訂：平裝

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

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21.

Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala

出版日：2018/01/31 作者：Md. Rezaul Karim 出版社：Packt Pub Ltd 裝訂：平裝

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.Key FeaturesExplore machine learning techniques with prominent ope

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Deep Learning Neural Networks ─ Design and Case Studies

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22.

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：平裝

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

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23.

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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24.

Neural Networks: Computational Models and Applications

出版日：2010/11/23 作者：Huajin Tang; Kay Chen Tan; Zhang Yi 出版社：Springer Verlag 裝訂：平裝

Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, variou

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Intersection and Decomposition Algorithms for Planar Arrangements

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25.

Intersection and Decomposition Algorithms for Planar Arrangements

出版日：2010/09/09 作者：Pankaj K. Agarwal 出版社：Cambridge Univ Pr 裝訂：平裝

Several geometric problems can be formulated in terms of the arrangement of a collection of curves in a plane, which has made this one of the most widely studied topics in computational geometry. This book, first published in 1991, presents a study of various problems related to arrangements of lines, segments, or curves in the plane. The first problem is a proof of almost tight bounds on the length of (n,s)-Davenport–Schinzel sequences, a technique for obtaining optimal bounds for numerous algorithmic problems. Then the intersection problem is treated. The final problem is improving the efficiency of partitioning algorithms, particularly those used to construct spanning trees with low stabbing numbers, a very versatile tool in solving geometric problems. A number of applications are also discussed. Researchers in computational and combinatorial geometry should find much to interest them in this book.

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26.

Iterative Computer Algorithms With Applications In Engineering: Solving Combinatorial Optimization Problems

出版日：2000/01/27 作者：Sait 出版社：John Wiley & Sons Inc 裝訂：平裝

Iterative Computer Algorithms with Applications in Engineering describes in-depth the five main iterative algorithms for solving hard combinatorial optimization problems: Simulated Annealing, Genetic

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Decision Making Technique in Construction Management

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27.

Decision Making Technique in Construction Management

出版日：2007/06/01 作者：C. M. TAM; Thomas K. L. TONG; H. ZHANG 出版社：香港城市大學出版社 裝訂：平裝

Decision Making and Operations Research Techniques for Construction Management challenges the traditional empirical approach to problem solving and provides construction practitioners with a seasoned

優惠價： 79 830

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28.

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

29.

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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30.

Applications of Computational Intelligence in Multi-Disciplinary Research

出版日：2021/11/01 作者：Ahmed A. Elngar(EDI) 出版社：ACADEMIC PR INC 裝訂：平裝

Applications of Computational Intelligence in Multi-Disciplinary Research provides readers with a comprehensive handbook for applying the powerful principles, concepts and algorithms of Computational

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Beyond the Worst-Case Analysis of Algorithms

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31.

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.

優惠價： 9 3023

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Machine Learning Refined ― Foundations, Algorithms, and Applications

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32.

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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33.

Source Separation and Machine Learning

出版日：2018/11/01 作者：Jen-tzung Chien 出版社：Academic Pr 裝訂：平裝

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It il

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34.

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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35.

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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36.

Machine Learning Projects for .net Developers

出版日：2015/06/29 作者：Mathias Brandewinder 出版社：Springer Verlag 裝訂：平裝

Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-

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37.

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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Evaluating Learning Algorithms ― A Classification Perspective

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38.

Evaluating Learning Algorithms ― A Classification Perspective

出版日：2014/06/05 作者：Nathalie Japkowicz 出版社：Cambridge Univ Pr 裝訂：平裝

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for c

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39.

Introduction to Computer Science Using Python ─ A Computational Problem-Solving Focus

出版日：2012/12/10 作者：DIERBACH 出版社：JOHN WILEY & SONS;LTD 裝訂：平裝

Introduction to Computer Science Using Python: A Computational Problem-Solving Focus introduces readers to programming and computational problem-solving via a back-to-basics, step-by-step, objects-lat

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Computational Geometry ― Algorithms and Applications

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40.

Computational Geometry ― Algorithms and Applications

出版日：2010/11/19 作者：Mark De Berg; Otfried Cheong; Marc Van Kreveld; Mark Overmars 出版社：Springer Verlag 裝訂：平裝

This introduction to computational geometry focuses on algorithms. Motivation is provided from the application areas as all techniques are related to particular applications in robotics, graphics, CAD

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Machine Learning Algorithms for Problem Solving in Computational Applications