
000 | 00000cam u2200205 a 4500 | |
001 | 000045945659 | |
005 | 20180702174146 | |
008 | 180628s2018 flu b 001 0 eng d | |
010 | ▼a 2017023471 | |
020 | ▼a 9781138626782 (hardback : acid-free paper) | |
035 | ▼a (KERIS)REF000018461794 | |
040 | ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009 | |
050 | 0 0 | ▼a TK5105.59 ▼b .S735 2018 |
082 | 0 0 | ▼a 004.6 ▼2 23 |
084 | ▼a 004.6 ▼2 DDCK | |
090 | ▼a 004.6 ▼b S7832i | |
100 | 1 | ▼a Stamp, Mark. |
245 | 1 0 | ▼a Introduction to machine learning with applications in information security / ▼c Mark Stamp, San Jose State University, California. |
260 | ▼a Boca Raton : ▼b CRC Press, Taylor & Francis Group, ▼c c2018. | |
300 | ▼a xiv, 345 p. ; ▼c 25 cm. | |
490 | 1 | ▼a Chapman & Hall/CRC machine learning & pattern recognition ; ▼v 16 |
504 | ▼a Includes bibliographical references (p. 319-337) and index. | |
650 | 0 | ▼a Information networks ▼x Security measures. |
650 | 0 | ▼a Machine learning. |
830 | 0 | ▼a Chapman & Hall/CRC machine learning & pattern recognition ; ▼v 16. |
945 | ▼a KLPA |
소장정보
No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
---|---|---|---|---|---|---|---|
No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 004.6 S7832i | 등록번호 121245134 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
저자소개
목차
Introduction
What is Machine Learning? ?
About This Book?
Necessary Background
A Few Too Many Notes
I TOOLS OF THE TRADE
A Revealing Introduction to Hidden Markov Models
Introduction and Background
A Simple Example
Notation
The Three Problems
The Three Solutions
Dynamic Programming ?
Scaling?
All Together Now
The Bottom Line?
A Full Frontal View of Profile Hidden Markov Models?
Introduction
Overview and Notation
Pairwise Alignment
Multiple Sequence Alignment
PHMM from MSA
Scoring
The Bottom Line
Principal Components of Principal Component Analysis
Introduction?
Background
Principal Component Analysis ?
SVD Basics ?
All Together Now
A Numerical Example ?
The Bottom Line?
A Reassuring Introduction to Support Vector Machines
Introduction?
Constrained Optimization
AC loser Look at SVM
All Together Now?
A Note on Quadratic Programming?
The Bottom Line?
Problems ?
A Comprehensible Collection of Clustering Concepts
Introduction
Overview and Background
-Means
Measuring Cluster Quality
EM Clustering
The Bottom Line
Problems
Many Mini Topics
Introduction
-Nearest Neighbors
Neural Networks
Boosting
Random Forest
Linear Discriminant Analysis
VectorQuantization
Naive Bayes
Regression Analysis
Conditional Random Fields
Data Analysis
Introduction
Experimental Design
Accuracy
ROC Curves
Imbalance Problem
PR Curves
The Bottom Line
II APPLICATIONS
HMM Applications
Introduction
English Text Analysis ?
Detecting "Undetectable" Malware?
Classic Cryptanalysis
PHMM Applications
Introduction
Masquerade Detection
Malware Detection
PCA Applications
Introduction
Eigenfaces
Eigenviruses
Eigenspam
SVM Applications
Introduction
Malware Detection
Image Spam Revisited
Clustering Applications
Introduction
-Means for Malware Classification
EM vs -Means for Malware Analysis
정보제공 :
