Machine learning and visual perception:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin ; Boston
De Gruyter
[2020]
[Peking] Tsinghua University Press [2020] |
| Schriftenreihe: | De Gruyter STEM
|
| Schlagworte: | |
| Online-Zugang: | https://www.degruyter.com/doc/cover/9783110595536.jpg http://www.degruyter.com/search?f_0=isbnissn&q_0=9783110595536&searchTitles=true Inhaltsverzeichnis Klappentext |
| Beschreibung: | VIII, 142 Seiten Illustrationen 24 cm x 17 cm |
| ISBN: | 9783110595536 3110595532 |
Internformat
MARC
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| 245 | 1 | 0 | |a Machine learning and visual perception |c Baochang Zhang, Ce Li, Nana Lin |
| 264 | 1 | |a Berlin ; Boston |b De Gruyter |c [2020] | |
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| 653 | |a Fachpublikum/ Wissenschaft | ||
| 653 | |a Fachpublikum/ Wissenschaft | ||
| 653 | |a COM004000 | ||
| 653 | |a COM021030: COM021030 COMPUTERS / Database Management / Data Mining | ||
| 653 | |a COM032000: COM032000 COMPUTERS / Information Technology | ||
| 653 | |a COM051300: COM051300 COMPUTERS / Programming / Algorithms | ||
| 653 | |a TEC067000: TEC067000 Technology & Engineering / Signals & Signal Processing | ||
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| 700 | 1 | |a Lin, Nana |e Verfasser |4 aut | |
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Datensatz im Suchindex
| _version_ | 1804180594033164288 |
|---|---|
| adam_text | Contents Introduction — 1 1 1.1 1.1.1 1.1.2 1.1.3 1.1.4 1.1.5 1.1.6 1.1.6.1 1.1.6.2 1.1.6.3 1.1.6.4 1.1.7 1.2 1.2.1 1.2.2 1.2.3 1.3 1.3.1 1.3.2 1.3.3 2 2.1 2.1.1 2.1.2 2.1.3 2.2 2.2.1 2.2.2 3 3.1 Introduction of machine learning — 3 Introduction — 3 Machine learning — 3 Basic concepts — 3 Definition and significance — 4 History of machine learning-----5 Machine learning system — 6 Basic elements of the machine learning system — 6 Category of machine learning — 7 Classification based on learning strategies — 7 Classification based on the representation of acquired knowledge-----9 Classification based on application area —11 Comprehensive classification —11 Current research field-----13 Statistical pattern recognition —14 Problem representation-----15 Experience risk minimization-----16 Complexity and generalization-----17 Core theory of statistical learning-----19 Consistency condition of the learning process —19 Generalization bounds —19 Structural risk minimization — 22 Summary — 24 PAC Model-----25 Introduction — 25 Basic model-----25 Introduction of PAC-----25 Basic concepts — 26 Problematic — 26 Sample complexity in the PAC model-----27 Sample complexity in finite space-----27 Sample complexity in infinite space — 29 Decision tree learning—33 Introduction — 33 Overview of a decision tree — 33
VI —— Contents 3.1.1 3.1.2 3.1.3 3.1.4 3.2 3.2.1 3.2.2 4 4.1 4.1.1 4.1.2 4.1.3 4.1.4 4.2 4.2.1 4.2.2 4.2.3 4.3 4.3.1 4.3.2 4.3.3 4.3.4 5 5.1 5.2 5.3 5.4 6 6.1 6.1.1 6.1.2 6.2 6.2.1 Decision tree-----34 Property-----36 Application-----36 Learning-----37 Design of decision tree------37 Characteristics of decision trees — 37 Generation of decision trees — 38 Summary-----45 Bayesian learning — 47 Introduction-----47 Bayesian learning — 47 Bayesian formula — 47 Minimum error decision-----48 Normal probability density — 49 Maximum likelihood estimation — 50 Naive Bayesian principle and application — 51 Bayesian best hypothesis — 51 Naive Bayesian classification — 52 Text classification based on Naive Bayes — 53 Hidden Markov model and application — 56 Markov property — 56 Markov chain — 56 Transition probability matrix-----57 Hidden Markov model and application — 57 Summary — 60 Support vector machines — 63 Introduction-----63 Support vector machines-----63 Implementation algorithm — 69 SVM example-----71 Multi-class SVM-----73 Summary — 74 AdaBoost-----75 Introduction-----75 AdaBoost and object detection — 75 AdaBoost algorithm — 75 AdaBoost initialization — 77 Robust real-time object detection — 80 Rectangular feature selectioh — 80
Contents 6.2.2 6.2.3 6.2.4 6.3 Integral image — 81 Training result — 82 Cascade----- 82 Object detection using statistical learning theory----- 85 6.4 6.4.1 Random forest — 86 Principle description----- 86 6.4.2 Algorithm details----- 86 6.4.3 Algorithms analysis-----86 Summary----- 87 7 Compressed sensing-----89 Introduction----- 89 7.1 Theory framework----- 89 7.2 7.2.1 7.2.2 Basic theory and core issues------91 Mathematical model — 91 Signal sparse representation----- 91 7.2.3 Signal observation matrix----- 92 7.2.4 7.3 7.3.1 7.3.2 8 Signal reconstruction algorithm----- 93 Application and simulation — 94 Application — 94 Face recognition-----95 Summary----- 97 Subspace learning — 99 Introduction — 99 8.1 Feature extraction based on PCA----- 99 8.2 Mathematical model —102 8.3 8.3.1 Mathematical calculation of PCA —103 Conclusions of linear algebra--- 103 8.3.2 8.3.3 Eigenvalue decomposition based on the covariance matrix----- 104 PCA----- 104 8.4 Property of PCA----- 105 8.5 Face recognition based on PCA —107 Summary —107 9 Deep learning and neural networks —109 Introduction----- 109 9.1 9.1.1 Neural network----- 109 Forward neural network —109 9.1.2 Perceptron network----- 109 9.1.3 Three-layer forward neural network----- 112 9.1.4 BP algorithm —112 VII
VIII — Contents 9.2 9.2.1 9.2.2 9.2.3 9.2.4 9.3 9.3.1 9.3.2 Deep learning —116 Overview of deep learning----- 116 Auto-Encoder algorithm----- 117 Auto-Encoder deep network —118 Convolution neural network —119 Applications of deep learning —124 Binarized convolutional networks for classification Time-series recognition----- 124 Summary —125 IO Reinforcement learning----- 127 Introduction —127 10.1 10.2 10.2.1 10.2.2 10.2.3 10.2.4 10.2.5 10.2.6 10.2.7 10.2.8 10.3 Overview of reinforcement learning —-127 Process of reinforcement learning — 128 Markov property —128 Reward----- 129 Value function----- 129 Dynamic programming —130 MC method —130 Temporal difference learning —131 Q-learning —132 Improved Q-learning----- 135 Code implementation----- 137 Bibliography —141 Index-— 143 124
Machine !Jtirmnfi timi Visuel Ivrccpfém provides an up-to-date oven iov on the topie, including the PAC modei, decision tree, Bayesian (earning, support vector machines, Ada Boost, compressive sensing, subspace learning, neural networks as weil as deep and rein forcemeat learning. Both classic and moel algorithms are introduced in classifier design, lace recognition, deep learning, tinte series recogni tion. image classification, ami object deseci ion. The inclusion ot numerous practical examples makes this book an use Гін reference for students, researchers, teachers and readers interested in computer science and machine learning. ► Systematic presentation of machine learning and visual perception, covering both classic and recen! algonífi.-ns, ► Extensive implementation of examples to facilitate understanding. ► Presentation of advanced theories, fundamentals and new research directions in easV lo-ııiKİerstand v-’ay՝. Baochang Zhang, of Technology where he achieved his PhD in computer science. Now he is з tenured associate professor at Beihang University, China. His current research interests indude deep learning, pattern recognition, object tracking, radar signal analysis, face recognition, and wavelets. Nana i.ln, studied in Harbin Institute of Technology where she achieved her master degree in computer science. At present she is project manager in Surfilter Network Technology Co., Ltd. C .l f.!. received her PhD in computer Chinese Academy of Sciences, China. She is currently a lecturer at China University of Mining arid Technology. Her current research interests
include computer vision, video analysis, and machine learning.
|
| any_adam_object | 1 |
| author | Zhang, Baocheng Li, Ce Lin, Nana |
| author_GND | (DE-588)1032682280 |
| author_facet | Zhang, Baocheng Li, Ce Lin, Nana |
| author_role | aut aut aut |
| author_sort | Zhang, Baocheng |
| author_variant | b z bz c l cl n l nl |
| building | Verbundindex |
| bvnumber | BV046206443 |
| classification_rvk | ST 300 |
| classification_tum | DAT 760f DAT 708f |
| ctrlnum | (OCoLC)1164636309 (DE-599)DNB1165802376 |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV046206443 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:38:14Z |
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| institution_GND | (DE-588)10095502-2 |
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| language | English |
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| spelling | Zhang, Baocheng Verfasser (DE-588)1032682280 aut Machine learning and visual perception Baochang Zhang, Ce Li, Nana Lin Berlin ; Boston De Gruyter [2020] [Peking] Tsinghua University Press [2020] © 2020 VIII, 142 Seiten Illustrationen 24 cm x 17 cm txt rdacontent n rdamedia nc rdacarrier De Gruyter STEM Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Paperback / softback Fachpublikum/ Wissenschaft COM004000 COM021030: COM021030 COMPUTERS / Database Management / Data Mining COM032000: COM032000 COMPUTERS / Information Technology COM051300: COM051300 COMPUTERS / Programming / Algorithms TEC067000: TEC067000 Technology & Engineering / Signals & Signal Processing UMB: Algorithms & data structures UN: Databases UNC: Data capture & analysis UT: Computer networking & communications UYQ: Artificial intelligence UYS: Signal processing 1632: Hardcover, Softcover / Informatik, EDV/Informatik Maschinelles Lernen (DE-588)4193754-5 s DE-604 Li, Ce Verfasser aut Lin, Nana Verfasser aut Walter de Gruyter GmbH & Co. KG (DE-588)10095502-2 pbl Erscheint auch als Online-Ausgabe, PDF 978-3-11-059556-7 Erscheint auch als Online-Ausgabe, EPUB 978-3-11-059322-8 X:MVB https://www.degruyter.com/doc/cover/9783110595536.jpg X:MVB http://www.degruyter.com/search?f_0=isbnissn&q_0=9783110595536&searchTitles=true Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031585433&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031585433&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Zhang, Baocheng Li, Ce Lin, Nana Machine learning and visual perception Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Machine learning and visual perception |
| title_auth | Machine learning and visual perception |
| title_exact_search | Machine learning and visual perception |
| title_full | Machine learning and visual perception Baochang Zhang, Ce Li, Nana Lin |
| title_fullStr | Machine learning and visual perception Baochang Zhang, Ce Li, Nana Lin |
| title_full_unstemmed | Machine learning and visual perception Baochang Zhang, Ce Li, Nana Lin |
| title_short | Machine learning and visual perception |
| title_sort | machine learning and visual perception |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen |
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