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出版者:Wiley-Interscience

作者:Vladimir N. Vapnik

出品人:

页数:768

译者:

出版时间:1998-9-30

价格:USD 221.00

装帧:Hardcover

isbn号码:9780471030034

丛书系列:

图书标签:

• 统计学习
• 机器学习
• Statistics
• MachineLearning
• 数学
• Vapnik
• 统计学
• Theory
• Statistical Learning Theory
• Machine Learning
• Theory of Learning
• Data Analysis
• Statistics
• Algorithms
• Mathematics
• Deep Learning

Statistical Learning Theory: A Comprehensive Exploration of Algorithmic Decision Making and Pattern Recognition This book offers a deep dive into the foundational principles and advanced methodologies of statistical learning theory. It meticulously unravels the intricate relationship between data, algorithms, and the ability to make informed decisions or discern patterns within complex datasets. Far from being a mere collection of algorithms, this text focuses on the why and how behind successful learning, providing readers with a robust theoretical framework that underpins a wide spectrum of modern data-driven applications. The journey begins with an exploration of the fundamental concepts that define statistical learning. We meticulously define what it means for a system to "learn" from data, distinguishing between supervised, unsupervised, and reinforcement learning paradigms. The book delves into the core challenge of model generalization – how a model trained on a finite set of observations can effectively predict outcomes for unseen data. This critical aspect is examined through the lens of bias-variance trade-off, a central theme that permeates the entire work. Readers will gain a profound understanding of how the complexity of a model impacts its ability to capture underlying trends without overfitting to noise. Key statistical concepts are introduced and developed rigorously. Probability theory forms the bedrock, with detailed discussions on random variables, probability distributions, and key statistical moments. We then build upon this foundation to explore concepts like maximum likelihood estimation and Bayesian inference, presenting them not just as computational techniques but as principled approaches to parameter estimation and model selection. The notion of risk minimization is central to the theoretical development, with detailed analyses of different risk functions and their implications for learning. The book dedicates significant attention to the theoretical underpinnings of various learning algorithms, moving beyond superficial descriptions to expose the mathematical machinery that drives their performance. For instance, in the context of regression, we meticulously analyze the properties of linear and non-linear regression models, exploring concepts like the representer theorem and the role of regularization in preventing overfitting. The intricacies of classification are similarly dissected, with thorough examinations of methods such as logistic regression, support vector machines (SVMs), and the fundamental principles behind decision trees. The geometric interpretations and optimization landscapes associated with these algorithms are elucidated, providing an intuitive yet mathematically sound grasp of their behavior. A substantial portion of the text is devoted to the crucial area of model complexity and its relationship with generalization. We introduce and analyze foundational concepts such as VC-dimension, Rademacher complexity, and covering numbers. These powerful theoretical tools allow us to quantify the "capacity" of a learning algorithm and derive bounds on generalization error, providing rigorous guarantees for learning performance. The book explains how these abstract measures translate into practical considerations when choosing models and designing learning strategies. Furthermore, the book delves into the realm of kernel methods, explaining how they enable learning in high-dimensional feature spaces implicitly. The underlying theory of reproducing kernel Hilbert spaces (RKHS) is explored, providing a solid mathematical foundation for understanding the power and flexibility of kernel-based learning. This section illuminates how seemingly simple algorithms can achieve remarkable performance by transforming data into richer representations. The text also addresses the challenges and advancements in dealing with large datasets. Concepts related to online learning and stochastic optimization are presented, highlighting algorithms designed for scenarios where data arrives sequentially or is too massive to process in its entirety. We discuss the theoretical properties of these methods, including their convergence rates and generalization bounds in the context of limited computational resources. Beyond individual algorithm analysis, the book explores ensemble methods, such as bagging and boosting. The theoretical justifications for their superior performance are meticulously examined, explaining how combining multiple weak learners can lead to a significantly stronger and more robust predictive model. The fundamental principles behind their success, rooted in variance reduction and bias reduction respectively, are clearly articulated. Throughout the text, the emphasis is on developing a deep, intuitive understanding coupled with rigorous mathematical exposition. The aim is to equip readers not only with the ability to use learning algorithms but to understand their strengths, limitations, and underlying theoretical guarantees. This foundational knowledge is essential for researchers and practitioners seeking to push the boundaries of machine learning, develop novel algorithms, and critically evaluate the performance of existing methods in diverse and challenging real-world applications. The book serves as an indispensable resource for anyone aspiring to master the theoretical underpinnings of statistical learning.

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Statistical Learning Theory这本书是一本完整阐述了统计机器学习思想的名著。在该书中作者对统计机器学习和传统机器学习的区别的本质进行了详细的论证，并且指出统计机器学习能够对训练样本给出精确的学习效果，并能够回答训练过程需要的样本训练数等一系列问题。

评分☆☆☆☆☆

Statistical Learning Theory这本书是一本完整阐述了统计机器学习思想的名著。在该书中作者对统计机器学习和传统机器学习的区别的本质进行了详细的论证，并且指出统计机器学习能够对训练样本给出精确的学习效果，并能够回答训练过程需要的样本训练数等一系列问题。

评分☆☆☆☆☆

Statistical Learning Theory这本书是一本完整阐述了统计机器学习思想的名著。在该书中作者对统计机器学习和传统机器学习的区别的本质进行了详细的论证，并且指出统计机器学习能够对训练样本给出精确的学习效果，并能够回答训练过程需要的样本训练数等一系列问题。

评分☆☆☆☆☆

Statistical Learning Theory这本书是一本完整阐述了统计机器学习思想的名著。在该书中作者对统计机器学习和传统机器学习的区别的本质进行了详细的论证，并且指出统计机器学习能够对训练样本给出精确的学习效果，并能够回答训练过程需要的样本训练数等一系列问题。

评分☆☆☆☆☆

Statistical Learning Theory这本书是一本完整阐述了统计机器学习思想的名著。在该书中作者对统计机器学习和传统机器学习的区别的本质进行了详细的论证，并且指出统计机器学习能够对训练样本给出精确的学习效果，并能够回答训练过程需要的样本训练数等一系列问题。

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坦白讲，这本书的难度是毋庸置疑的，它绝非轻松的下午茶读物。它要求读者具备扎实的线性代数和微积分基础，否则在早期阶段就会感到吃力。然而，正是这种高门槛，保证了内容的纯粹性和深度。书中对贝叶斯学习框架的探讨，不仅仅是停留在公式堆砌，而是深入到了信念更新和信息论的交汇点，这种跨学科的视角非常启发思考。我感觉，每一次攻克书中的一个小节，都像是完成了一次智力上的攀登。它迫使我走出舒适区，去重新审视那些我自以为已经掌握的知识点，发现了许多过去忽略的细节和假设。对于那些渴望真正掌握统计学习的理论基础，并希望在未来的研究或高级应用中游刃有余的专业人士，这本书提供了一种无与伦比的、系统性的训练，是真正意义上的“内功心法”修炼指南。

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这本书简直是机器学习领域的“圣经”！从概率论的基础到复杂的非参数方法，作者构建了一个极其严谨而又直观的知识体系。我尤其欣赏它在理论深度上的挖掘，而不是仅仅停留在算法的表层介绍。例如，在讨论模型泛化能力时，书中对VC维、Rademacher复杂度的阐述深入浅出，即便是初次接触这些概念的读者，也能在扎实的数学推导后领悟其精髓。它并没有回避那些令人望而生畏的数学证明，而是巧妙地将其穿插在清晰的逻辑脉络中，让你感觉每一步推导都是为了更好地理解“为什么”这个模型会有效，而不仅仅是“如何”应用它。这种对理论本质的追求，使得它区别于市面上大量偏重工程实践的教材。读完之后，我对回归、分类、聚类等基础任务的底层逻辑有了全新的认识，不再满足于调用现成的库函数，而是能真正理解其背后的局限性和适用范围。这本书更像是一位严谨的导师，带着你一步步构建起完整的统计学习思维框架，为后续深入研究奠定了坚实的基础。

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对于那些希望从“工具使用者”蜕变为“理论设计者”的进阶学习者来说，这本书简直是量身定做。它没有过多渲染深度学习的华丽外表，而是将笔墨集中于那些跨越了技术潮流、更具普适性的学习原则。书中对核方法（Kernel Methods）的介绍尤为出色，它清晰地阐释了将低维数据映射到高维特征空间以实现线性可分性的强大魔力，以及如何通过核技巧避免显式的高维计算，这在处理非线性问题时具有极其重要的理论指导意义。我特别喜欢书中关于“经验风险最小化”与“结构风险最小化”的区分，这直接指向了模型选择和正则化的核心矛盾。每一次阅读似乎都能发现新的层次感，仿佛剥开了一层又一层的洋葱皮，最终触及到统计推断的本质。这本书的价值不在于它教了你多少算法，而在于它教会了你如何批判性地看待和设计新的学习算法。

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这是一部需要静下心来仔细研读的著作，它不适合那些期望快速上手搭建一个AI模型便大功告成的读者。其叙事节奏相对缓慢，但这种“慢”恰恰是构建深厚理解所必需的。书中对不同学习范式的对比分析极其精妙，比如有监督学习与无监督学习的哲学差异，以及如何在有限数据下权衡偏差与方差（Bias-Variance Trade-off）的艺术。阅读过程中，我发现它极大地拓宽了我对“学习”本身的定义，它不仅仅是拟合数据，更是一种关于信息压缩与不确定性量化的过程。作者在引入新概念时，总会先从一个具体的、容易理解的例子入手，比如决策树的递归划分，然后迅速过渡到更抽象的函数逼近理论，这种螺旋上升的结构设计非常高明。虽然阅读过程时有卡壳，需要反复查阅前面的定义，但这恰恰说明了内容的密度和相互关联性之强，让人不得不佩服作者在知识组织上的匠心独运。

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这本书的排版和图示设计简直是教科书级别的典范。尽管内容本身极其抽象和复杂，但作者通过精心设计的图形来辅助理解，极大地降低了学习曲线的陡峭程度。例如，在解释支持向量机（SVM）的几何意义时，书中用简洁的线条勾勒出了最大间隔超平面的构建过程，使得那些复杂的拉格朗日对偶问题似乎也变得可以触摸、可以直观感受了。这种对视觉辅助的重视，使得长时间的阅读体验也保持了较高的专注度。此外，书中章节之间的逻辑衔接非常流畅，几乎没有出现生硬的跳跃感。仿佛作者是一位技艺精湛的建筑师，每一章都是一个坚实的承重墙，共同支撑起整个理论大厦。如果你曾被其他晦涩难懂的数学著作劝退，这本书或许能让你重拾信心，因为它证明了严谨的理论也可以被优雅地呈现出来。

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经典之作

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很难啃的一本书，理论研究真的要做心理准备。

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很难啃的一本书，理论研究真的要做心理准备。

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这才是真正把理论上升到哲学方面的典范，大牛！

评分☆☆☆☆☆

很难啃的一本书，理论研究真的要做心理准备。