Sequential methods in pattern recognition and machine learning /:
Sequential methods in pattern recognition and machine learning.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
Academic Press,
1968.
|
| Schriftenreihe: | Mathematics in science and engineering ;
v. 52. |
| Schlagworte: | |
| Online-Zugang: | Volltext Volltext |
| Zusammenfassung: | Sequential methods in pattern recognition and machine learning. |
| Beschreibung: | 1 online resource (xi, 227 pages) |
| Format: | Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |
| Bibliographie: | Includes bibliographical references and indexes. |
| ISBN: | 9780080955599 0080955592 1282290193 9781282290198 |
Internformat
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| 533 | |a Electronic reproduction. |b [Place of publication not identified] : |c HathiTrust Digital Library, |d 2010. |5 MiAaHDL | ||
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| 583 | 1 | |a digitized |c 2010 |h HathiTrust Digital Library |l committed to preserve |2 pda |5 MiAaHDL | |
| 520 | |a Sequential methods in pattern recognition and machine learning. | ||
| 505 | 0 | |a Front Cover; Sequential Methods in Pattern Recognition and Machine Learning; Copyright Page; Contents; Preface; Chapter 1. Introduction; 1.1 Pattern Recognition; 1.2 Deterministic Classification Techniques; 1.3 Training in Linear Classifiers; 1.4 Statistical Classification Techniques; 1.5 Sequential Decision Model for Pattern Classification; 1.6 Learning in Sequential Pattern Recognition Systems; 1.7 Summary and Further Remarks; References; Chapter 2. Feature Selection and Feature Ordering; 2.1 Feature Selection and Ordering-Information Theoretic Approach | |
| 505 | 8 | |a 2.2 Feature Selection and Ordering-Karhunen-Loève Expansion2.3 Illustrative Examples; 2.4 Summary and Further Remarks; References; Chapter 3. Forward Procedure for Finite Sequential Classification Using Modified Sequential Probability Ratio Test; 3.1 Introduction; 3.2 Modified Sequential Probability Ratio Test-Discrete Case; 3.3 Modified Sequential Probability Ratio Test-Continuous Case; 3.4 Procedure of Modified Generalized Sequential Probability Ratio Test; 3.5 Experiments in Pattern Classification; 3.6 Summary and Further Remarks; References | |
| 505 | 8 | |a Chapter 4. Backward Procedure for Finite Sequential Recognition Using Dynamic Programming4.1 Introduction; 4.2 Mathematical Formulation and Basic Functional Equation; 4.3 Reduction of Dimensionality; 4.4 Experiments in Pattern Classification; 4.5 Backward Procedure for Both Feature Ordering and Pattern Classification; 4.6 Experiments in Feature Ordering and Pattern Classification; 4.7 Use of Dynamic Programming for Feature-Subset Selection; 4.8 Suboptimal Sequential Pattern Recognition; 4.9 Summary and Further Remarks; References | |
| 505 | 8 | |a Chapter 5. Nonparametric Procedure in Sequential Pattern Classification5.1 Introduction; 5.2 Sequential Ranks and Sequential Ranking Procedure; 5.3 A Sequential Two-Sample Test Problem; 5.4 Nonparametric Design of Sequential Pattern Classifiers; 5.5 Analysis of Optimal Performance and a Multiclass Generalization; 5.6 Experimental Results and Discussions; 5.7 Summary and Further Remarks; References; Chapter 6. Bayesian Learning in Sequential Pattern Recognition Systems; 6.1 Supervised Learning Using Bayesian Estimation Techniques; 6.2 Nonsupervised Learning Using Bayesian Estimation Techniques | |
| 505 | 8 | |a 6.3 Bayesian Learning of Slowly Varying Patterns6.4 Learning of Parameters Using an Empirical Bayes Approach; 6.5 A General Model for Bayesian Learning Systems; 6.6 Summary and Further Remarks; References; Chapter 7. Learning in Sequential Recognition Systems Using Stochastic Approximation; 7.1 Supervised Learning Using Stochastic Approximation; 7.2 Nonsupervised Learning Using Stochastic Approximation; 7.3 A General Formulation of Nonsupervised Learning Systems Using Stochastic Approximation; 7.4 Learning of Slowly Time-Varying Parameters Using Dynamic Stochastic Approximation | |
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| author | Fu, K. S. (King Sun), 1930-1985 |
| author_GND | http://id.loc.gov/authorities/names/n80046074 |
| author_facet | Fu, K. S. (King Sun), 1930-1985 |
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| contents | Front Cover; Sequential Methods in Pattern Recognition and Machine Learning; Copyright Page; Contents; Preface; Chapter 1. Introduction; 1.1 Pattern Recognition; 1.2 Deterministic Classification Techniques; 1.3 Training in Linear Classifiers; 1.4 Statistical Classification Techniques; 1.5 Sequential Decision Model for Pattern Classification; 1.6 Learning in Sequential Pattern Recognition Systems; 1.7 Summary and Further Remarks; References; Chapter 2. Feature Selection and Feature Ordering; 2.1 Feature Selection and Ordering-Information Theoretic Approach 2.2 Feature Selection and Ordering-Karhunen-Loève Expansion2.3 Illustrative Examples; 2.4 Summary and Further Remarks; References; Chapter 3. Forward Procedure for Finite Sequential Classification Using Modified Sequential Probability Ratio Test; 3.1 Introduction; 3.2 Modified Sequential Probability Ratio Test-Discrete Case; 3.3 Modified Sequential Probability Ratio Test-Continuous Case; 3.4 Procedure of Modified Generalized Sequential Probability Ratio Test; 3.5 Experiments in Pattern Classification; 3.6 Summary and Further Remarks; References Chapter 4. Backward Procedure for Finite Sequential Recognition Using Dynamic Programming4.1 Introduction; 4.2 Mathematical Formulation and Basic Functional Equation; 4.3 Reduction of Dimensionality; 4.4 Experiments in Pattern Classification; 4.5 Backward Procedure for Both Feature Ordering and Pattern Classification; 4.6 Experiments in Feature Ordering and Pattern Classification; 4.7 Use of Dynamic Programming for Feature-Subset Selection; 4.8 Suboptimal Sequential Pattern Recognition; 4.9 Summary and Further Remarks; References Chapter 5. Nonparametric Procedure in Sequential Pattern Classification5.1 Introduction; 5.2 Sequential Ranks and Sequential Ranking Procedure; 5.3 A Sequential Two-Sample Test Problem; 5.4 Nonparametric Design of Sequential Pattern Classifiers; 5.5 Analysis of Optimal Performance and a Multiclass Generalization; 5.6 Experimental Results and Discussions; 5.7 Summary and Further Remarks; References; Chapter 6. Bayesian Learning in Sequential Pattern Recognition Systems; 6.1 Supervised Learning Using Bayesian Estimation Techniques; 6.2 Nonsupervised Learning Using Bayesian Estimation Techniques 6.3 Bayesian Learning of Slowly Varying Patterns6.4 Learning of Parameters Using an Empirical Bayes Approach; 6.5 A General Model for Bayesian Learning Systems; 6.6 Summary and Further Remarks; References; Chapter 7. Learning in Sequential Recognition Systems Using Stochastic Approximation; 7.1 Supervised Learning Using Stochastic Approximation; 7.2 Nonsupervised Learning Using Stochastic Approximation; 7.3 A General Formulation of Nonsupervised Learning Systems Using Stochastic Approximation; 7.4 Learning of Slowly Time-Varying Parameters Using Dynamic Stochastic Approximation |
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Introduction; 1.1 Pattern Recognition; 1.2 Deterministic Classification Techniques; 1.3 Training in Linear Classifiers; 1.4 Statistical Classification Techniques; 1.5 Sequential Decision Model for Pattern Classification; 1.6 Learning in Sequential Pattern Recognition Systems; 1.7 Summary and Further Remarks; References; Chapter 2. Feature Selection and Feature Ordering; 2.1 Feature Selection and Ordering-Information Theoretic Approach</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.2 Feature Selection and Ordering-Karhunen-Loève Expansion2.3 Illustrative Examples; 2.4 Summary and Further Remarks; References; Chapter 3. 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Backward Procedure for Finite Sequential Recognition Using Dynamic Programming4.1 Introduction; 4.2 Mathematical Formulation and Basic Functional Equation; 4.3 Reduction of Dimensionality; 4.4 Experiments in Pattern Classification; 4.5 Backward Procedure for Both Feature Ordering and Pattern Classification; 4.6 Experiments in Feature Ordering and Pattern Classification; 4.7 Use of Dynamic Programming for Feature-Subset Selection; 4.8 Suboptimal Sequential Pattern Recognition; 4.9 Summary and Further Remarks; References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Chapter 5. Nonparametric Procedure in Sequential Pattern Classification5.1 Introduction; 5.2 Sequential Ranks and Sequential Ranking Procedure; 5.3 A Sequential Two-Sample Test Problem; 5.4 Nonparametric Design of Sequential Pattern Classifiers; 5.5 Analysis of Optimal Performance and a Multiclass Generalization; 5.6 Experimental Results and Discussions; 5.7 Summary and Further Remarks; References; Chapter 6. Bayesian Learning in Sequential Pattern Recognition Systems; 6.1 Supervised Learning Using Bayesian Estimation Techniques; 6.2 Nonsupervised Learning Using Bayesian Estimation Techniques</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">6.3 Bayesian Learning of Slowly Varying Patterns6.4 Learning of Parameters Using an Empirical Bayes Approach; 6.5 A General Model for Bayesian Learning Systems; 6.6 Summary and Further Remarks; References; Chapter 7. 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| genre | Electronic book. |
| genre_facet | Electronic book. |
| id | ZDB-4-EBA-ocn316566894 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:16:42Z |
| institution | BVB |
| isbn | 9780080955599 0080955592 1282290193 9781282290198 |
| language | English |
| oclc_num | 316566894 |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xi, 227 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 1968 |
| publishDateSearch | 1968 |
| publishDateSort | 1968 |
| publisher | Academic Press, |
| record_format | marc |
| series | Mathematics in science and engineering ; |
| series2 | Mathematics in science and engineering ; |
| spelling | Fu, K. S. (King Sun), 1930-1985. https://id.oclc.org/worldcat/entity/E39PBJqBrjRpQdRWGtRG8YR7pP http://id.loc.gov/authorities/names/n80046074 Sequential methods in pattern recognition and machine learning / K.S. Fu. New York : Academic Press, 1968. 1 online resource (xi, 227 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Mathematics in science and engineering ; v. 52 Includes bibliographical references and indexes. Print version record. Use copy Restrictions unspecified star MiAaHDL Electronic reproduction. [Place of publication not identified] : HathiTrust Digital Library, 2010. MiAaHDL Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL Sequential methods in pattern recognition and machine learning. Front Cover; Sequential Methods in Pattern Recognition and Machine Learning; Copyright Page; Contents; Preface; Chapter 1. Introduction; 1.1 Pattern Recognition; 1.2 Deterministic Classification Techniques; 1.3 Training in Linear Classifiers; 1.4 Statistical Classification Techniques; 1.5 Sequential Decision Model for Pattern Classification; 1.6 Learning in Sequential Pattern Recognition Systems; 1.7 Summary and Further Remarks; References; Chapter 2. Feature Selection and Feature Ordering; 2.1 Feature Selection and Ordering-Information Theoretic Approach 2.2 Feature Selection and Ordering-Karhunen-Loève Expansion2.3 Illustrative Examples; 2.4 Summary and Further Remarks; References; Chapter 3. Forward Procedure for Finite Sequential Classification Using Modified Sequential Probability Ratio Test; 3.1 Introduction; 3.2 Modified Sequential Probability Ratio Test-Discrete Case; 3.3 Modified Sequential Probability Ratio Test-Continuous Case; 3.4 Procedure of Modified Generalized Sequential Probability Ratio Test; 3.5 Experiments in Pattern Classification; 3.6 Summary and Further Remarks; References Chapter 4. Backward Procedure for Finite Sequential Recognition Using Dynamic Programming4.1 Introduction; 4.2 Mathematical Formulation and Basic Functional Equation; 4.3 Reduction of Dimensionality; 4.4 Experiments in Pattern Classification; 4.5 Backward Procedure for Both Feature Ordering and Pattern Classification; 4.6 Experiments in Feature Ordering and Pattern Classification; 4.7 Use of Dynamic Programming for Feature-Subset Selection; 4.8 Suboptimal Sequential Pattern Recognition; 4.9 Summary and Further Remarks; References Chapter 5. Nonparametric Procedure in Sequential Pattern Classification5.1 Introduction; 5.2 Sequential Ranks and Sequential Ranking Procedure; 5.3 A Sequential Two-Sample Test Problem; 5.4 Nonparametric Design of Sequential Pattern Classifiers; 5.5 Analysis of Optimal Performance and a Multiclass Generalization; 5.6 Experimental Results and Discussions; 5.7 Summary and Further Remarks; References; Chapter 6. Bayesian Learning in Sequential Pattern Recognition Systems; 6.1 Supervised Learning Using Bayesian Estimation Techniques; 6.2 Nonsupervised Learning Using Bayesian Estimation Techniques 6.3 Bayesian Learning of Slowly Varying Patterns6.4 Learning of Parameters Using an Empirical Bayes Approach; 6.5 A General Model for Bayesian Learning Systems; 6.6 Summary and Further Remarks; References; Chapter 7. Learning in Sequential Recognition Systems Using Stochastic Approximation; 7.1 Supervised Learning Using Stochastic Approximation; 7.2 Nonsupervised Learning Using Stochastic Approximation; 7.3 A General Formulation of Nonsupervised Learning Systems Using Stochastic Approximation; 7.4 Learning of Slowly Time-Varying Parameters Using Dynamic Stochastic Approximation Perceptrons. http://id.loc.gov/authorities/subjects/sh85099714 Statistical decision. http://id.loc.gov/authorities/subjects/sh85127565 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Operations research. http://id.loc.gov/authorities/subjects/sh85095020 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Computer science. http://id.loc.gov/authorities/subjects/sh89003285 Electronic Data Processing Operations Research Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Perceptrons. Prise de décision (Statistique) Apprentissage automatique. Informatique. Recherche opérationnelle. computer science. aat data processing. aat COMPUTERS Optical Data Processing. bisacsh Operations research fast Electronic data processing fast Computer science fast Machine learning fast Perceptrons fast Statistical decision fast Electronic book. has work: Sequential methods in pattern recognition and machine learning (Text) https://id.oclc.org/worldcat/entity/E39PCFT7QJ4WJQdFfCR9RVcqw3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Fu, K.S. (King Sun), 1930- Sequential methods in pattern recognition and machine learning. New York : Academic Press, 1968 9780122695506 (DLC) 68008424 (OCoLC)435344 Mathematics in science and engineering ; v. 52. http://id.loc.gov/authorities/names/n42015986 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=297029 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/bookseries/00765392/52 Volltext |
| spellingShingle | Fu, K. S. (King Sun), 1930-1985 Sequential methods in pattern recognition and machine learning / Mathematics in science and engineering ; Front Cover; Sequential Methods in Pattern Recognition and Machine Learning; Copyright Page; Contents; Preface; Chapter 1. Introduction; 1.1 Pattern Recognition; 1.2 Deterministic Classification Techniques; 1.3 Training in Linear Classifiers; 1.4 Statistical Classification Techniques; 1.5 Sequential Decision Model for Pattern Classification; 1.6 Learning in Sequential Pattern Recognition Systems; 1.7 Summary and Further Remarks; References; Chapter 2. Feature Selection and Feature Ordering; 2.1 Feature Selection and Ordering-Information Theoretic Approach 2.2 Feature Selection and Ordering-Karhunen-Loève Expansion2.3 Illustrative Examples; 2.4 Summary and Further Remarks; References; Chapter 3. Forward Procedure for Finite Sequential Classification Using Modified Sequential Probability Ratio Test; 3.1 Introduction; 3.2 Modified Sequential Probability Ratio Test-Discrete Case; 3.3 Modified Sequential Probability Ratio Test-Continuous Case; 3.4 Procedure of Modified Generalized Sequential Probability Ratio Test; 3.5 Experiments in Pattern Classification; 3.6 Summary and Further Remarks; References Chapter 4. Backward Procedure for Finite Sequential Recognition Using Dynamic Programming4.1 Introduction; 4.2 Mathematical Formulation and Basic Functional Equation; 4.3 Reduction of Dimensionality; 4.4 Experiments in Pattern Classification; 4.5 Backward Procedure for Both Feature Ordering and Pattern Classification; 4.6 Experiments in Feature Ordering and Pattern Classification; 4.7 Use of Dynamic Programming for Feature-Subset Selection; 4.8 Suboptimal Sequential Pattern Recognition; 4.9 Summary and Further Remarks; References Chapter 5. Nonparametric Procedure in Sequential Pattern Classification5.1 Introduction; 5.2 Sequential Ranks and Sequential Ranking Procedure; 5.3 A Sequential Two-Sample Test Problem; 5.4 Nonparametric Design of Sequential Pattern Classifiers; 5.5 Analysis of Optimal Performance and a Multiclass Generalization; 5.6 Experimental Results and Discussions; 5.7 Summary and Further Remarks; References; Chapter 6. Bayesian Learning in Sequential Pattern Recognition Systems; 6.1 Supervised Learning Using Bayesian Estimation Techniques; 6.2 Nonsupervised Learning Using Bayesian Estimation Techniques 6.3 Bayesian Learning of Slowly Varying Patterns6.4 Learning of Parameters Using an Empirical Bayes Approach; 6.5 A General Model for Bayesian Learning Systems; 6.6 Summary and Further Remarks; References; Chapter 7. Learning in Sequential Recognition Systems Using Stochastic Approximation; 7.1 Supervised Learning Using Stochastic Approximation; 7.2 Nonsupervised Learning Using Stochastic Approximation; 7.3 A General Formulation of Nonsupervised Learning Systems Using Stochastic Approximation; 7.4 Learning of Slowly Time-Varying Parameters Using Dynamic Stochastic Approximation Perceptrons. http://id.loc.gov/authorities/subjects/sh85099714 Statistical decision. http://id.loc.gov/authorities/subjects/sh85127565 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Operations research. http://id.loc.gov/authorities/subjects/sh85095020 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Computer science. http://id.loc.gov/authorities/subjects/sh89003285 Electronic Data Processing Operations Research Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Perceptrons. Prise de décision (Statistique) Apprentissage automatique. Informatique. Recherche opérationnelle. computer science. aat data processing. aat COMPUTERS Optical Data Processing. bisacsh Operations research fast Electronic data processing fast Computer science fast Machine learning fast Perceptrons fast Statistical decision fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85099714 http://id.loc.gov/authorities/subjects/sh85127565 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85095020 http://id.loc.gov/authorities/subjects/sh85042288 http://id.loc.gov/authorities/subjects/sh89003285 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Sequential methods in pattern recognition and machine learning / |
| title_auth | Sequential methods in pattern recognition and machine learning / |
| title_exact_search | Sequential methods in pattern recognition and machine learning / |
| title_full | Sequential methods in pattern recognition and machine learning / K.S. Fu. |
| title_fullStr | Sequential methods in pattern recognition and machine learning / K.S. Fu. |
| title_full_unstemmed | Sequential methods in pattern recognition and machine learning / K.S. Fu. |
| title_short | Sequential methods in pattern recognition and machine learning / |
| title_sort | sequential methods in pattern recognition and machine learning |
| topic | Perceptrons. http://id.loc.gov/authorities/subjects/sh85099714 Statistical decision. http://id.loc.gov/authorities/subjects/sh85127565 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Operations research. http://id.loc.gov/authorities/subjects/sh85095020 Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Computer science. http://id.loc.gov/authorities/subjects/sh89003285 Electronic Data Processing Operations Research Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Perceptrons. Prise de décision (Statistique) Apprentissage automatique. Informatique. Recherche opérationnelle. computer science. aat data processing. aat COMPUTERS Optical Data Processing. bisacsh Operations research fast Electronic data processing fast Computer science fast Machine learning fast Perceptrons fast Statistical decision fast |
| topic_facet | Perceptrons. Statistical decision. Machine learning. Operations research. Electronic data processing. Computer science. Electronic Data Processing Operations Research Machine Learning Prise de décision (Statistique) Apprentissage automatique. Informatique. Recherche opérationnelle. computer science. data processing. COMPUTERS Optical Data Processing. Operations research Electronic data processing Computer science Machine learning Perceptrons Statistical decision Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=297029 https://www.sciencedirect.com/science/bookseries/00765392/52 |
| work_keys_str_mv | AT fuks sequentialmethodsinpatternrecognitionandmachinelearning |