| 000 | nam a22 7a 4500 | ||
|---|---|---|---|
| 999 |
_c29100 _d29100 |
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| 008 | 180727b xxu||||| |||| 00| 0 eng d | ||
| 020 | _a9781138626782 | ||
| 082 |
_a004.6 _bSTA |
||
| 100 | _aStamp, Mark | ||
| 245 | _aIntroduction to machine learning with applications in information security | ||
| 260 |
_bCRC Press, _c2018 _aBoca Raton: |
||
| 300 |
_axiv, 345 p. : _bill.; _c24 cm. |
||
| 365 |
_aGBP _b49.99 |
||
| 440 | _aChapman & Hall/CRC machine learning & pattern recognition | ||
| 520 | _aIntroduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis. Most of the examples in the book are drawn from the field of information security, with many of the machine learning applications specifically focused on malware. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Many of the exercises in this book require some programming, and basic computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of programming experience should have no trouble with this aspect of the book. Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/. For the reader’s benefit, the figures in the book are also available in electronic form, and in color. | ||
| 650 | _aMachine Learning | ||
| 650 | _aDynamic Programming | ||
| 650 | _aScaling | ||
| 650 | _aMarkov Models | ||
| 650 | _aPHMM | ||
| 650 | _aMSA | ||
| 650 | _aNeural Networks | ||
| 650 | _aLinear Discriminant Analysis | ||
| 650 | _aData Analysis | ||
| 650 | _aROC Curves | ||
| 650 | _aMalware | ||
| 650 | _aSpam | ||
| 650 | _aHMM Applications | ||
| 650 | _aClassic Cryptanalysis | ||
| 650 | _aPR Curves | ||
| 650 | _aClustering | ||
| 650 | _aPrincipal Component Analysis | ||
| 650 | _aQuadratic Programming | ||
| 650 | _aSVD | ||
| 942 |
_2ddc _cBK |
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