Description: 机器学习的中文版本，作者Tom M. Mitchell。这本教材的目标是展现机器学习中核心的算法和理论。机器学习从很多学科吸收了成果和概念，包括统计学、人工智能、哲学、信息论、生物学、认知科学、计算复杂性和控制论等。-Machine learning of the Chinese version, the author Tom M. Mitchell. The teaching objective is to demonstrate the core of machine learning algorithms and theory. Machine learning from many disciplines to absorb the results and concepts, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity and control theory. Platform: |
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Author:kengibon |
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Description: This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below). Extensive support is provided for course instructors. Platform: |
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Author:arcanesky |
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Description: Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.-Learning Kernel Classifiers : Theory and Algorithms. Introduction This chapter introduces the gene the acidic problem of machine learning and how it relat es to statistical inference. 1.1 The Learning P roblem and (Statistical) It was only inference a few years after the introduction of the first c omputer that one of man's greatest dreams seeme d to be realizable-artificial intelligence. B earing in mind that in the early days the most pow erful computers had much less computational po wer than a cell phone today, it comes as no surprise that much theoretical're search on the potential of machines' capabilit ies to learn took place at this time. This become 's a computational problem as soon as the dataset gets larger than a few hundred examples. Platform: |
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Description: 贝叶斯（Bayesion）机器学习相互信息。用于求取熵、联合熵等。改进后可用于随机反演-Bayesian (Bayesion) machine learning of mutual information. Used to obtain entropy, joint entropy and so on. Improved can be used for stochastic inversion Platform: |
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Author:Leo |
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Description: tom mitchell写的机器学习，人工智能研究必看，含有众多基础算法，浅显易懂-machine learning,written by tom mitchell, artificial intelligence research must-see, contains a number of basic algorithms, easy to understand Platform: |
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Author:吴奇 |
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Description: 《数据迁移学习》09年新书，模式识别与机器学习领域的人必备的新书，相信这是本领域最新最好的书籍之一。-" Data Migration Learning" in 2009 book, pattern recognition and machine learning fields to essential new book, I believe this is the latest in this field, one of the best books. Platform: |
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Author:gouyabin |
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Description: 《机器学习与数据挖掘方法和应用》，讨论数据挖掘和知识发现中的有关问题及多策略学习方法，具体地阐述了机器学习与数据挖掘在工程设计，文本，图像和音乐，网页分析，计算机病毒和计算机控制，医疗诊断，生物医疗信号分析和水质分析中的生物信号处理等方面的应用情况，根据实例说明具体解决方法。-" Machine learning and data mining methods and applications" to discuss the data mining and knowledge discovery issues and multi-strategy learning methods, specifically describes the machine learning and data mining in engineering design, text, images and music, web analysis, computer viruses and computer control, medical diagnostics, biomedical signal analysis and quality analysis of biological signal processing and other applications, according to examples of specific solutions. Platform: |
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Author:iris_su |
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Description: tom mitchell 翻译来的机器学习中文版。中文的哦~读起来满顺的。书写得很有趣，跟大家分享-tom mitchell machine learning to translate the Chinese version. Oh- read the full Chinese Shun. Writing very interesting to share with you ~ ~ Platform: |
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Author:chenliang |
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Description: 本书从机器学习的角度介绍了基于视觉的运动分析领域的最新算法和系统。-Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition.
Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most act Platform: |
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Author:kankan |
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Description: 在这个练习中，您将实现线性回归，并看到它在数据上工作。在开始这个编程练习之前，我们强烈建议观看视频讲座，并完成相关主题的复习题。开始锻炼，你将需要下载的启动代码，解压其内容目录到你希望完成练习。如果需要的话，在开始练习之前，使用八度/ matlab中的cd命令改变这个目录。你也可以?ND指示在“环境设置说明”的课程网站安装倍频/ MATLAB。(In this exercise, you will implement linear regression and get to see it work on data. Before starting on this programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics. To get started with the exercise, you will need to download the starter code and unzip its contents to the directory where you wish to complete the exercise. If needed, use the cd command in Octave/MATLAB to change to this directory before starting this exercise.) Platform: |
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Description: Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the
help of this comprehensive cookbook(TensorFlow was open sourced in November of 2015 by Google, and since then it has become
the most starred machine learning repository on GitHub. TensorFlow's popularity is due to the
approach of creating computational graphs, automatic differentiation, and customizability.
Because of these features, TensorFlow is a very powerful and adaptable tool that can be used
to solve many different machine learning problems.) Platform: |
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Author:liming123a
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