LEADER 01418nam 2200421 450 001 9910794125603321 005 20220411040539.0 010 $a1-4529-6085-2 035 $a(CKB)4100000011267859 035 $a(OCoLC)1145108163 035 $a(MiAaPQ)EBC6208857 035 $a(EXLCZ)994100000011267859 100 $a20200824d2020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aThinking plant animal human $eencounters with communities of difference /$fDavid Wood 210 1$aMinneapolis, Minnesota ;$aLondon :$cUniversity of Minnesota Press,$d[2020] 210 4$d©2020 215 $a1 online resource (xix, 237 pages) $cillustrations 225 1 $aPosthumanities ;$v56 311 $a1-5179-0722-5 320 $aIncludes bibliographical references and index. 330 $a"Collected essays by a leading philosopher situating the question of the animal in the broader context of a relational ontology"--$cProvided by publisher. 410 0$aPosthumanities ;$v56. 606 $aAnimals (Philosophy) 615 0$aAnimals (Philosophy) 676 $a113/.8 700 $aWood$b David$f1946-$0861814 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910794125603321 996 $aThinking plant animal human$93750143 997 $aUNINA LEADER 06163nam 2200697 a 450 001 9910813232703321 005 20200520144314.0 010 $a1-119-96140-8 010 $a1-283-28311-5 010 $a9786613283115 010 $a1-118-30535-3 010 $a1-119-95295-6 010 $a1-119-95296-4 035 $a(CKB)2550000000052719 035 $a(EBL)819173 035 $a(OCoLC)763160180 035 $a(SSID)ssj0000541559 035 $a(PQKBManifestationID)11351597 035 $a(PQKBTitleCode)TC0000541559 035 $a(PQKBWorkID)10499089 035 $a(PQKB)11435366 035 $a(MiAaPQ)EBC819173 035 $a(Au-PeEL)EBL819173 035 $a(CaPaEBR)ebr10500952 035 $a(CaONFJC)MIL328311 035 $a(PPN)243652593 035 $a(EXLCZ)992550000000052719 100 $a20110715d2011 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical pattern recognition /$fAndrew R. Webb, Keith D. Copsey 205 $a3rd ed. 210 $aHoboken $cWiley$d2011 215 $a1 online resource (xxiv, 642 pages) $cillustrations, tables 300 $aDescription based upon print version of record. 311 $a0-470-68227-2 311 $a0-470-68228-0 320 $aIncludes bibliographical references and index. 327 $aStatistical Pattern Recognition; Contents; Preface; Notation; 1 Introduction to Statistical Pattern Recognition; 1.1 Statistical Pattern Recognition; 1.1.1 Introduction; 1.1.2 The Basic Model; 1.2 Stages in a Pattern Recognition Problem; 1.3 Issues; 1.4 Approaches to Statistical Pattern Recognition; 1.5 Elementary Decision Theory; 1.5.1 Bayes' Decision Rule for Minimum Error; 1.5.2 Bayes' Decision Rule for Minimum Error - Reject Option; 1.5.3 Bayes' Decision Rule for Minimum Risk; 1.5.4 Bayes' Decision Rule for Minimum Risk - Reject Option; 1.5.5 Neyman-Pearson Decision Rule 327 $a1.5.6 Minimax Criterion1.5.7 Discussion; 1.6 Discriminant Functions; 1.6.1 Introduction; 1.6.2 Linear Discriminant Functions; 1.6.3 Piecewise Linear Discriminant Functions; 1.6.4 Generalised Linear Discriminant Function; 1.6.5 Summary; 1.7 Multiple Regression; 1.8 Outline of Book; 1.9 Notes and References; Exercises; 2 Density Estimation - Parametric; 2.1 Introduction; 2.2 Estimating the Parameters of the Distributions; 2.2.1 Estimative Approach; 2.2.2 Predictive Approach; 2.3 The Gaussian Classifier; 2.3.1 Specification; 2.3.2 Derivation of the Gaussian Classifier Plug-In Estimates 327 $a2.3.3 Example Application Study2.4 Dealing with Singularities in the Gaussian Classifier; 2.4.1 Introduction; 2.4.2 Na ??ve Bayes; 2.4.3 Projection onto a Subspace; 2.4.4 Linear Discriminant Function; 2.4.5 Regularised Discriminant Analysis; 2.4.6 Example Application Study; 2.4.7 Further Developments; 2.4.8 Summary; 2.5 Finite Mixture Models; 2.5.1 Introduction; 2.5.2 Mixture Models for Discrimination; 2.5.3 Parameter Estimation for Normal Mixture Models; 2.5.4 Normal Mixture Model Covariance Matrix Constraints; 2.5.5 How Many Components?; 2.5.6 Maximum Likelihood Estimation via EM 327 $a2.5.7 Example Application Study2.5.8 Further Developments; 2.5.9 Summary; 2.6 Application Studies; 2.7 Summary and Discussion; 2.8 Recommendations; 2.9 Notes and References; Exercises; 3 Density Estimation - Bayesian; 3.1 Introduction; 3.1.1 Basics; 3.1.2 Recursive Calculation; 3.1.3 Proportionality; 3.2 Analytic Solutions; 3.2.1 Conjugate Priors; 3.2.2 Estimating the Mean of a Normal Distribution with Known Variance; 3.2.3 Estimating the Mean and the Covariance Matrix of a Multivariate Normal Distribution; 3.2.4 Unknown Prior Class Probabilities; 3.2.5 Summary; 3.3 Bayesian Sampling Schemes 327 $a3.3.1 Introduction3.3.2 Summarisation; 3.3.3 Sampling Version of the Bayesian Classifier; 3.3.4 Rejection Sampling; 3.3.5 Ratio of Uniforms; 3.3.6 Importance Sampling; 3.4 Markov Chain Monte Carlo Methods; 3.4.1 Introduction; 3.4.2 The Gibbs Sampler; 3.4.3 Metropolis-Hastings Algorithm; 3.4.4 Data Augmentation; 3.4.5 Reversible Jump Markov Chain Monte Carlo; 3.4.6 Slice Sampling; 3.4.7 MCMC Example - Estimation of Noisy Sinusoids; 3.4.8 Summary; 3.4.9 Notes and References; 3.5 Bayesian Approaches to Discrimination; 3.5.1 Labelled Training Data; 3.5.2 Unlabelled Training Data 327 $a3.6 Sequential Monte Carlo Samplers 330 $a"Statistical Pattern Recognition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book describes techniques for analysing data comprising measurements made on individuals or objects. The techniques are used to make a prediction such as disease of a patient, the type of object illuminated by a radar, economic forecast. Emphasis is placed on techniques for classification, a term used for predicting the class or group an object belongs to (based on a set of exemplars) and for methods that seek to discover natural groupings in a data set. Each section concludes with a description of the wide range of practical applications that have been addressed and the further developments of theoretical techniques and includes a variety of exercises, from 'open-book' questions to more lengthy projects. New material is presented, including the analysis of complex networks and basic techniques for analysing the properties of datasets and also introduces readers to the use of variational methods for Bayesian density estimation and looks at new applications in biometrics and security"--$cProvided by publisher. 606 $aPattern perception$xStatistical methods 615 0$aPattern perception$xStatistical methods. 676 $a006.4 686 $aMAT029000$2bisacsh 700 $aWebb$b A. R$g(Andrew R.)$01644174 701 $aCopsey$b Keith D$0915398 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910813232703321 996 $aStatistical pattern recognition$93989889 997 $aUNINA LEADER 05977nam 22007455 450 001 9910483523903321 005 20251226195828.0 010 $a3-319-10172-2 024 7 $a10.1007/978-3-319-10172-9 035 $a(CKB)3710000000219449 035 $a(SSID)ssj0001338644 035 $a(PQKBManifestationID)11780378 035 $a(PQKBTitleCode)TC0001338644 035 $a(PQKBWorkID)11337728 035 $a(PQKB)10861375 035 $a(DE-He213)978-3-319-10172-9 035 $a(MiAaPQ)EBC6295817 035 $a(MiAaPQ)EBC5588129 035 $a(Au-PeEL)EBL5588129 035 $a(OCoLC)889234051 035 $a(PPN)180626094 035 $a(EXLCZ)993710000000219449 100 $a20140812d2014 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aBusiness Process Management $e12th International Conference, BPM 2014, Haifa, Israel, September 7-11, 2014, Proceedings /$fedited by Shazia Sadiq, Pnina Soffer, Hagen Völzer 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (XVI, 434 p. 141 illus.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v8659 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-319-10171-4 327 $aPart: Declarative Processes -- Monitoring Business Metaconstraints Based on LTL and LDL for Finite Traces -- Hierarchical Declarative Modelling with Refinement and Sub-processes -- Discovering Target-Branched Declare Constraints -- Part: User-Centered Process Approaches -- Crowd-Based Mining of Reusable Process Model Patterns -- A Recommender System for Process Discovery -- Listen to Me: Improving Process Model Matching through User Feedback -- Part: Process Discovery -- Beyond Tasks and Gateways: Discovering BPMN Models with Subprocesses, Boundary Events and Activity Markers -- A Genetic Algorithm for Process Discovery Guided by Completeness, Precision and Simplicity -- Constructs Competition Miner: Process Control-Flow Discovery of BP-Domain Constructs -- Part: Integrative BPM -- Chopping Down Trees vs. Sharpening the Axe ? Balancing the Development of BPM Capabilities with Process Improvement -- Implicit BPM: A Business Process Platform for Transparent Workflow Weaving -- Modeling Concepts for Internal Controls in Business Processes ? An Empirically Grounded Extension of BPMN -- Part: Resource and Time Management in BPM -- Mining Resource Scheduling Protocols -- Dealing with Changes of Time-Aware Processes -- Temporal Anomaly Detection in Business Processes -- Part: Process Analytics -- A General Framework for Correlating Business Process Characteristics -- Behavioral Comparison of Process Models Based on Canonically Reduced Event Structures -- Where Did I Go Wrong? Explaining Errors in Business Process Models -- Part: Industry Papers -- User-Friendly Property Specification and Process Verification ? A Case Study with Vehicle-Commissioning Processes -- Analysis of Operational Data for Expertise Aware Staffing -- From a Family of State-Centric PAIS to a Configurable and Parameterized Business Process Architecture -- Part: Short Papers: Process Enabled Environments -- DRain: An Engine for Quality-of-Result Driven Process-Based Data Analytics -- Use Your Best Device! Enabling DeviceChanges at Runtime -- Specifying Flexible Human Behavior in Interaction-Intensive Process Environments -- Separating Execution and Data Management: A Key to Business Process-as-a-Service (BPaaS) -- Assessing the Need for Visibility of Business Processes ? A Process Visibility Fit Framework -- Part: Short Papers: Discovery and Monitoring -- The Automated Discovery of Hybrid Processes -- Declarative Process Mining: Reducing Discovered Models Complexity by Pre-Processing Event Logs -- SECPI: Searching for Explanations for Clustered Process Instances -- Business Monitoring Framework for Process Discovery with Real-Life Logs -- Predictive Task Monitoring for Business Processes. 330 $aThis book constitutes the proceedings of the 12th International Conference on Business Process Management, BPM 2014, held in Haifa, Israel, in September 2014. The 21 regular papers and 10 short papers included in this volume were carefully reviewed and selected from 123 submissions. 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