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LEADER 00000cam a2200601Ii 4500 
003    OCoLC 
005    20190319060810.4 
006    m     o  d         
007    cr cnu|||unuuu 
008    170419s2017    nyua    oe    000 0 eng d 
019    SpringerEBAocn982735679 
020    9781489976871|q(electronic bk.) 
020    1489976876|q(electronic bk.) 
020    9781489976864|q(print and electronic bundle) 
020    1489976868 
020    9781489976864 
020    |z9781489976857|q(print) 
020    |z1489976868 
020    |z148997685X 
024 7  10.1007/978-1-4899-7687-1|2doi 
024 3  9781489976864 
037    9781489976864|b00024965 
040    GW5XE|beng|erda|epn|cGW5XE|dOCLCF|dYDX|dUAB|dESU|dJG0|dIOG
       |dOTZ|dQGK|dVT2|dU3W|dCAUOI|dOCLCQ|dCOO|dKSU|dOCLCQ|dEBLCP
       |dWYU|dAUD 
049    MAIN 
050  4 Q325.5 
082 04 006.3/1|223 
245 00 Encyclopedia of machine learning and data mining /|cClaude
       Sammut, Geoffrey I. Webb, editors. 
250    Second edition. 
264  1 New York, NY :|bSpringer,|c2017. 
300    1 online resource :|billustrations (some color) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
505 0  Intro; Preface; Contributors; A; A/B Testing; Abduction; 
       Definition; Motivation and Background; Structure of the 
       Learning Task; Abduction in Artificial Intelligence; 
       Abductive Concept Learning; Abduction and Induction; 
       Abduction in Systems Biology; Cross-References; 
       Recommended Reading; Absolute Error Loss; Accuracy; 
       Definition; Cross-References; ACO; Actions; Active 
       Learning; Definition; Structure of Learning System; 
       Related Problems; Active Learning Scenarios; Constructive 
       Active Learning; Pool-Based Active Learning; Stream-Based 
       Active Learning; Other Forms of Active Learning 
505 8  Common Active Learning StrategiesStatistical Active 
       Learning; The Need for Reference Distributions; A Detailed
       Example: Statistical Active Learning with LOESS; Greedy 
       Versus Batch Active Learning; Cross-References; 
       Recommended Reading; Active Learning Theory; Definition; 
       Learning from Labeled and Unlabeled Data; Motivating 
       Examples; Example: Thresholds on the Line; Example: Linear
       Separators in R2; Example: An Overabundance of Unlabeled 
       Data; The Sample Complexity of Active Learning; Generic 
       Results for Separable Data; Mildly Selective Sampling; A 
       Bayesian Model; Other Work; Conclusion 
505 8  Cross-ReferencesRecommended Reading; Adaboost; Adaptive 
       Control Processes; Adaptive Learning; Adaptive Real-Time 
       Dynamic Programming; Synonyms; Definition; Motivation and 
       Background; Structure of Learning System; Backup 
       Operations; Off-Line Versus On-Line; Learning A Model; 
       Summary of Theoretical Results; Special Cases and 
       Extensions; Cross-References; Recommended Reading; 
       Adaptive Resonance Theory; Adaptive Resonance Theory; ART 
       Design Elements; Stable Fast Learning with Distributed and
       Winner-Take-All Coding; Complement Coding: Learning Both 
       Absent Features and Present Features 
505 8  Matching, Attention, and SearchApplications; The Boston 
       Testbed; Application 1: Learning from Experience with Self
       -Supervised ART; Application 2: Transforming Information 
       into Knowledge Using ART Knowledge Discovery; Application 
       3: Correcting Errors by Biasing Attention Using Biased 
       ART; Future Directions; New Paradigms for Autonomous 
       Intelligent Systems: Complementary Computing and Laminar 
       Computing; Complementary Computing in the Design of 
       Perceptual/Cognitive and Spatial/Motor Systems 
505 8  Where's Waldo? Unifying Spatial and Object Attention, 
       Learning, Recognition, and Search of Valued Objects and 
       ScenesGeneral-Purpose Vision and How It Supports Object 
       Learning, Recognition, and Tracking; Visual and Spatial 
       Navigation, Cognitive Working Memory, and Planning; Social
       Cognition; Mental Disorders and Homeostatic Plasticity; 
       Machine Consciousness?; Recommended Reading; Adaptive 
       System; Agent; Agent-Based Computational Models; Agent-
       Based Modeling and Simulation; Agent-Based Simulation 
       Models; AIS; Algorithm Evaluation; Definition; Motivation 
       and Background; Processes and Techniques 
520 8  Annotation|bThis comprehensive encyclopedia, in A-Z format,
       provides easy access to relevant information for those 
       seeking entry into any aspect within the broad field of 
       machine learning. Most of the several hundred entries 
       include useful literature references, providing the reader
       with a portal to more detailed information on any given 
       topic. 
650  0 Machine learning|vEncyclopedias. 
650  0 Data mining|vEncyclopedias. 
655  4 Electronic books. 
655  7 Encyclopedias.|2fast|0(OCoLC)fst01423798 
700 1  Sammut, Claude,|d1956-|eeditor. 
700 1  Webb, Geoffrey I.,|eeditor. 
710 2  SpringerLink|eissuing body. 
776 08 |iPrint version:|tEncyclopedia of machine learning and 
       data mining.|bSecond edition|z9781489976857
       |w(OCoLC)1001572348 
830  0 Springer Computer Science eBooks 2017 English+
       International 
856 40 |uhttps://ezp.lib.unimelb.edu.au/login?url=http://
       link.springer.com/10.1007/978-1-4899-7687-1|zConnect to 
       ebook (University of Melbourne only) 
990    Springer EBA e-book collections for 2017-2019 
990    Springer Computer Science eBooks 2017 - Full Set 
990    Batch Ebook load (bud2) - do not edit, delete or attach 
       any records. 
991    |zNEW New collection springerlink.ebookscs2017 2019-03-18 
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