Pattern Recognition And Machine Learning یکی از کتب آموزشی و منابع دانشگاهی برای شناسایی الگو در تصاویر ماهواره ای و تصاویر هوایی است؛ که در آن به خوبی انواع الگوریتم های پایه یادگیری ماشین به خوبی توضیح داده شده است. این کتاب که توسط پروفسور کریستوفر بی شاپ نوشته شده به خوبی الگوریتم های نظارت شده و غیر نظارت شده، توضیح داده شده است.
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years.
In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models.
Also, the practical applicability of Bayesian methods have been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation.
Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning.
It is aimed at advanced undergraduates or first-year Ph.D. students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Because this book has a broad scope, it is impossible to provide a complete list of references, and in particular, no attempt has been made to provide accurate historical attribution of ideas.
Instead, the aim has been to give references that offer greater detail than is possible here and that hopefully provide entry points into what, in some cases, is a very extensive literature. For this reason, the references are often to more recent textbooks and review articles rather than original sources.
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حجم: 17.25 MB
نوع فايل: PDF
سال انتشار: 2006
رمز فایل: ندارد