File Size: 29267 KB
Print Length: 745 pages
Publisher: Springer; 2 edition (August 26, 2009)
Publication Date: August 26, 2009
The Elements of Statistical Learning is a comprehensive numerical treatment of machine studying from a statistical perspective. This means you acquire good derivations of well-known methods like support vector machines, random forests, in addition to graphical models; but every is developed only following the appropriate (and mistakenly considered less sexy) record framework has already already been derived (linear models, kernel smoothing, ensembles, and therefore on).
In addition in order to having excellent and correct mathematical derivations of crucial algorithms The Elements of Statistical Learning is rather unique inside that it actually makes use of the math to attain big things. My preferred examples are derived from Chapter three or more "Linear Techniques for Regression. inch The standard treatments regarding these methods depend heavily on respectful memorization regarding regurgitation of original iterative procedure definitions of typically the various regression methods. Within such a standard ingredients two regression methods are usually different if they have superficially different steps or even if different citation/priority chronicles. The Elements of Statistical Learning instead derives typically the stopping conditions of each method and considers methods typically the same if they produce exactly the same solution (regardless regarding how they claim they will do it) and even comes close consequences and outcomes of various methods. This hard usage of isomorphism allows amazing effects such as Figure three or more. 15 (which shows exactly how Least Angle Regression varies from Lasso regression, not merely in algorithm description or even history: but by selecting different models from typically the same data) and section 3. 5. 2 (which can separate Partial Least Squares' design CLAIM regarding fixing the x-dominance discovered in principle pieces evaluation from how effective this actually is as correcting such problems).
The greatest issue is who is usually the book for? This is a mathy publication emphasizing deep understanding above mere implementation. Unlike several lesser machine learning publications the math is not right now there for appearances or mere intimidating typesetting: it is usually there to allow typically the authors to organize many methods into a smaller number of steady designs. So I would the book is for scientists and machine algorithm designers. If you have a specific issue that is making inference difficult you may locate the perfect solution is in this publication. This is best for scientists but probably off-putting with regard to tinkers (as this guide likely has methods better than their current favorite new idea). The interested student may also benefit from this book, the derivations are usually done well which means you learn a lot by operating through them.
Finally- avoid buy the kindle edition, nevertheless the print book. This book is satisfying deep reading and you may want typically the benefits of the printed webpage (and Amazon's issues inside conversion usually are not the authors' fault)., great product thank you, This is THE publication to read/study-from/have-on-your-shelf-as-a-reference if an individual are serious about data technology, This book is stunning. It is definitely rather than an introductory book, but the hard work is worth it. This should probably be read alongside with an open Wikipedia tab, for all typically the non-statistics wizards out right now there. In summary, this publication takes effort to go through, but it is a invaluable jewel of knowledge in the field., This book is usually clear and concise in addition to using it using the web site lectures makes the studying easy and if an individual are a statistician with this problem, it is great with regard to the review, A very great book for machine learning. Worth reading once again and again and once again., great!, Great book with regard to stats student. Covers basic machine learning techniques.
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