مشخصات کلی Understanding machine learning : from theory to algorithms
نویسنده کتاب (Author):
Shai Shalev-Shwartz; Shai Ben-Davidدرخواست کتاب اورجینال
انتشارات (Publisher):
New York, NY, USA : Cambridge University Press, 2014.خرید و فروش فایل
ویرایش و نوع فایل (Edition/Format):
Print book : Document Computer File : English فروش فایل تخصصی
منبع (Database):
موضوع (Subject):
Machine learning. Algorithms. COMPUTERS — Computer Vision & Pattern Recognition. View all subjects
توضیحات خلاصه (Summary):
“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 an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed 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 an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering”– Read more…
اضافی فرمت فیزیکی:(DLC) 2014001779
(OCoLC)866619766
موضوع:Document
نوع منبع:Book, Computer File
تمام نویسندگان / همکاران: Shai Shalev-Shwartz; Shai Ben-David Find more information about: Shai Shalev-Shwartz Shai Ben-David
شناسه شابک ISBN:9781107298019 1107298016
شناسه OCLC:979269518
جزئیات Description:1 online resource (xvi, 397 pages ): illustrations.
فهرست محتوا:Introduction —
I. Foundations —
A gentle start —
A formal learning model —
Learning via uniform convergence —
The bias-complexity tradeoff —
The VC-dimension —
Nonuniform learnability —
The runtime of learning —
II. From Theory to Algorithms —
Linear predictors —
Boosting —
Model selection and validation —
Convex learning problems —
Regularization and stability —
Stochastic gradient descent —
Support vector machines —
Kernel methods —
Multiclass, ranking, and complex prediction problems —
Decision trees —
Nearest neighbor —
Neural networks —
III. Additional Learning Models —
Online learning —
Clustering —
Dimensionality reduction —
Generative models —
Feature selection and generation —
IV. Advanced Theory —
Rademacher complexities —
Covering numbers —
Proof of the fundamental theorem of learning theory —
Multiclass learnability —
Compression bounds —
PAC-Bayes.
مسئوليت Responsibility:Shai Shalev-Shwartz, the Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada.
