000			08342cam a2200697Ka 4500
001			ocn760091928
003			OCoLC
005			20171224113737.0
006			m o d
007			cr cnu---unuuu
008			111109s2011 enka ob 001 0 eng d
040			_aN$T _beng _epn _cN$T _dYDXCP _dDG1 _dTEFOD _dUPM _dOCLCQ _dOCLCF _dRRP _dTEFOD _dOCLCQ _dDG1
020			_a9781119952954 _q(electronic bk.)
020			_a1119952956 _q(electronic bk.)
020			_a9781119952961 _q(electronic bk.)
020			_a1119952964 _q(electronic bk.)
020			_z9780470682272
020			_z0470682272
020			_z9780470682289
020			_z0470682280
029	1		_aNZ1 _b14695716
029	1		_aNZ1 _b15290911
029	1		_aNZ1 _b15412280
035			_a(OCoLC)760091928
037			_aFF02F8CD-7AC4-4372-BFD9-3D8B02EE5199 _bOverDrive, Inc. _nhttp://www.overdrive.com
050		4	_aQ327 _bW43 2011eb
072		7	_aCOM _x047000 _2bisacsh
082	0	4	_a006.4 _223
049			_aMAIN
100	1		_aWebb, A. R. _q(Andrew R.)
245	1	0	_aStatistical pattern recognition.
250			_a3rd ed. / _bAndrew R. Webb, Keith D. Copsey, Gavin Cawley.
260			_aOxford : _bWiley-Blackwell, _c2011.
300			_a1 online resource (xxiv, 642 pages) : _billustrations
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aPrevious edition: New York: Wiley, 2002.
504			_aIncludes bibliographical references and index.
588	0		_aPrint version record.
505	0		_aNote continued: 9.3.Comparing Classifier Performance -- 9.3.1. Which Technique is Best? -- 9.3.2. Statistical Tests -- 9.3.3.Comparing Rules When Misclassification Costs are Uncertain -- 9.3.4. Example Application Study -- 9.3.5. Further Developments -- 9.3.6. Summary -- 9.4. Application Studies -- 9.5. Summary and Discussion -- 9.6. Recommendations -- 9.7. Notes and References -- Exercises -- 10. Feature Selection and Extraction -- 10.1. Introduction -- 10.2. Feature Selection -- 10.2.1. Introduction -- 10.2.2. Characterisation of Feature Selection Approaches -- 10.2.3. Evaluation Measures -- 10.2.4. Search Algorithms for Feature Subset Selection -- 10.2.5.Complete Search -- Branch and Bound -- 10.2.6. Sequential Search -- 10.2.7. Random Search -- 10.2.8. Markov Blanket -- 10.2.9. Stability of Feature Selection -- 10.2.10. Example Application Study -- 10.2.11. Further Developments -- 10.2.12. Summary -- 10.3. Linear Feature Extraction -- 10.3.1. Principal Components Analysis -- 10.3.2. Karhunen-Loeve Transformation -- 10.3.3. Example Application Study -- 10.3.4. Further Developments -- 10.3.5. Summary -- 10.4. Multidimensional Scaling -- 10.4.1. Classical Scaling -- 10.4.2. Metric MDS -- 10.4.3. Ordinal Scaling -- 10.4.4. Algorithms -- 10.4.5. MDS for Feature Extraction -- 10.4.6. Example Application Study -- 10.4.7. Further Developments -- 10.4.8. Summary -- 10.5. Application Studies -- 10.6. Summary and Discussion -- 10.7. Recommendations -- 10.8. Notes and References -- Exercises -- 11. Clustering -- 11.1. Introduction -- 11.2. Hierarchical Methods -- 11.2.1. Single-Link Method -- 11.2.2.Complete-Link Method -- 11.2.3. Sum-of-Squares Method -- 11.2.4. General Agglomerative Algorithm -- 11.2.5. Properties of a Hierarchical Classification -- 11.2.6. Example Application Study -- 11.2.7. Summary -- 11.3. Quick Partitions -- 11.4. Mixture Models -- 11.4.1. Model Description -- 11.4.2. Example Application Study -- 11.5. Sum-of-Squares Methods -- 11.5.1. Clustering Criteria -- 11.5.2. Clustering Algorithms -- 11.5.3. Vector Quantisation -- 11.5.4. Example Application Study -- 11.5.5. Further Developments -- 11.5.6. Summary -- 11.6. Spectral Clustering -- 11.6.1. Elementary Graph Theory -- 11.6.2. Similarity Matrices -- 11.6.3. Application to Clustering -- 11.6.4. Spectral Clustering Algorithm -- 11.6.5. Forms of Graph Laplacian -- 11.6.6. Example Application Study -- 11.6.7. Further Developments -- 11.6.8. Summary -- 11.7. Cluster Validity -- 11.7.1. Introduction -- 11.7.2. Statistical Tests -- 11.7.3. Absence of Class Structure -- 11.7.4. Validity of Individual Clusters -- 11.7.5. Hierarchical Clustering -- 11.7.6. Validation of Individual Clusterings -- 11.7.7. Partitions -- 11.7.8. Relative Criteria -- 11.7.9. Choosing the Number of Clusters -- 11.8. Application Studies -- 11.9. Summary and Discussion -- 11.10. Recommendations -- 11.11. Notes and References -- Exercises -- 12.Complex Networks -- 12.1. Introduction -- 12.1.1. Characteristics -- 12.1.2. Properties -- 12.1.3. Questions to Address -- 12.1.4. Descriptive Features -- 12.1.5. Outline -- 12.2. Mathematics of Networks -- 12.2.1. Graph Matrices -- 12.2.2. Connectivity -- 12.2.3. Distance Measures -- 12.2.4. Weighted Networks -- 12.2.5. Centrality Measures -- 12.2.6. Random Graphs -- 12.3.Community Detection -- 12.3.1. Clustering Methods -- 12.3.2. Girvan-Newman Algorithm -- 12.3.3. Modularity Approaches -- 12.3.4. Local Modularity -- 12.3.5. Clique Percolation -- 12.3.6. Example Application Study -- 12.3.7. Further Developments -- 12.3.8. Summary -- 12.4. Link Prediction -- 12.4.1. Approaches to Link Prediction -- 12.4.2. Example Application Study -- 12.4.3. Further Developments -- 12.5. Application Studies -- 12.6. Summary and Discussion -- 12.7. Recommendations -- 12.8. Notes and References -- Exercises -- 13. Additional Topics -- 13.1. Model Selection -- 13.1.1. Separate Training and Test Sets -- 13.1.2. Cross-Validation -- 13.1.3. The Bayesian Viewpoint -- 13.1.4. Akaike's Information Criterion -- 13.1.5. Minimum Description Length -- 13.2. Missing Data -- 13.3. Outlier Detection and Robust Procedures -- 13.4. Mixed Continuous and Discrete Variables -- 13.5. Structural Risk Minimisation and the Vapnik-Chervonenkis Dimension -- 13.5.1. Bounds on the Expected Risk -- 13.5.2. The VC Dimension.
520			_a"Statistical Pattern Recognition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book describes techniques for analysing data comprising measurements made on individuals or objects. The techniques are used to make a prediction such as disease of a patient, the type of object illuminated by a radar, economic forecast. Emphasis is placed on techniques for classification, a term used for predicting the class or group an object belongs to (based on a set of exemplars) and for methods that seek to discover natural groupings in a data set. Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques and includes a variety of exercises, from 'open-book' questions to more lengthy projects. New material is presented, including the analysis of complex networks and basic techniques for analysing the properties of datasets and also introduces readers to the use of variational methods for Bayesian density estimation and looks at new applications in biometrics and security."-- _cProvided by publisher.
520			_a"The book describes techniques for analysing data comprising measurements made on individuals or objects."-- _cProvided by publisher.
650		0	_aPattern perception _xStatistical methods.
650		0	_aDecision making _xMathematical models.
650		0	_aMathematical statistics.
650		7	_aCOMPUTERS _xOptical Data Processing. _2bisacsh
650		7	_aDecision making _xMathematical models. _2fast _0(OCoLC)fst00889048
650		7	_aMathematical statistics. _2fast _0(OCoLC)fst01012127
650		7	_aPattern perception _xStatistical methods. _2fast _0(OCoLC)fst01055263
655		4	_aElectronic books.
700	1		_aCopsey, Keith D.
700	1		_aCawley, Gavin.
776	0	8	_iPrint version: _aWebb, A.R. (Andrew R.). _tStatistical pattern recognition. _b3rd ed. _dOxford : Wiley-Blackwell, 2011 _z9780470682272 _w(OCoLC)751717289
856	4	0	_uhttp://onlinelibrary.wiley.com/book/10.1002/9781119952954 _zWiley Online Library
938			_aEBSCOhost _bEBSC _n398587
938			_aYBP Library Services _bYANK _n7188105
938			_aYBP Library Services _bYANK _n7461474
938			_aYBP Library Services _bYANK _n7598341
994			_a92 _bDG1
999			_c11374 _d11374