LEADER 01557nam 2200373 n 450 001 996391972903316 005 20221108040342.0 035 $a(CKB)1000000000673219 035 $a(EEBO)2240926340 035 $a(UnM)99844168 035 $a(EXLCZ)991000000000673219 100 $a19910815d1640 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 02$aA curious treatise of the nature and quality of chocolate. VVritten in Spanish by Antonio Colmenero, doctor in physicke and chirurgery. And put into English by Don Diego de Vades-forte$b[electronic resource] 210 $aImprinted at London $cBy I. Okes, dwelling in Little St. Bartholomewes$d1640 215 $a[6], 21, [1] p 300 $aDon Diego de Vades-forte = James Wadsworth. 300 $aA translation of: Curioso tratado de la naturaleza y calidad del chocolate. 300 $aRunning title reads: A curious treatise of chocolate. 300 $aReproduction of the original in the British Library. 330 $aeebo-0018 606 $aChocolate$vEarly works to 1800 615 0$aChocolate 700 $aColmenero de Ledesma$b Antonio$0796044 701 $aWadsworth$b James$f1604-1656?$01003794 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996391972903316 996 $aA curious treatise of the nature and quality of chocolate. VVritten in Spanish by Antonio Colmenero, doctor in physicke and chirurgery. And put into English by Don Diego de Vades-forte$92346024 997 $aUNISA LEADER 06825nam 22008415 450 001 9910484036603321 005 20251226203419.0 010 $a3-540-87481-X 024 7 $a10.1007/978-3-540-87481-2 035 $a(CKB)1000000000490767 035 $a(SSID)ssj0000715705 035 $a(PQKBManifestationID)11413778 035 $a(PQKBTitleCode)TC0000715705 035 $a(PQKBWorkID)10703646 035 $a(PQKB)10920533 035 $a(DE-He213)978-3-540-87481-2 035 $a(MiAaPQ)EBC3063242 035 $a(MiAaPQ)EBC6414054 035 $a(PPN)128126426 035 $a(EXLCZ)991000000000490767 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning and Knowledge Discovery in Databases $eEuropean Conference, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II /$fedited by Walter Daelemans, Katharina Morik 205 $a1st ed. 2008. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2008. 215 $a1 online resource (XXIII, 698 p.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v5212 300 $aIncludes index. 311 08$a3-540-87480-1 320 $aIncludes bibliographical references and index. 327 $aRegular Papers -- Exceptional Model Mining -- A Joint Topic and Perspective Model for Ideological Discourse -- Effective Pruning Techniques for Mining Quasi-Cliques -- Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain -- Fitted Natural Actor-Critic: A New Algorithm for Continuous State-Action MDPs -- A New Natural Policy Gradient by Stationary Distribution Metric -- Towards Machine Learning of Grammars and Compilers of Programming Languages -- Improving Classification with Pairwise Constraints: A Margin-Based Approach -- Metric Learning: A Support Vector Approach -- Support Vector Machines, Data Reduction, and Approximate Kernel Matrices -- Mixed Bregman Clustering with Approximation Guarantees -- Hierarchical, Parameter-Free Community Discovery -- A Genetic Algorithm for Text Classification Rule Induction -- Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness -- Kernel-Based Inductive Transfer -- State-Dependent Exploration for Policy Gradient Methods -- Client-Friendly Classification over Random Hyperplane Hashes -- Large-Scale Clustering through Functional Embedding -- Clustering Distributed Sensor Data Streams -- A Novel Scalable and Data Efficient Feature Subset Selection Algorithm -- Robust Feature Selection Using Ensemble Feature Selection Techniques -- Effective Visualization of Information Diffusion Process over Complex Networks -- Actively Transfer Domain Knowledge -- A Unified View of Matrix Factorization Models -- Parallel Spectral Clustering -- Classification of Multi-labeled Data: A Generative Approach -- Pool-Based Agnostic Experiment Design in Linear Regression -- Distribution-Free Learning of Bayesian Network Structure -- Assessing Nonlinear Granger Causality from Multivariate Time Series -- Clustering Via Local Regression -- Decomposable Families of Itemsets -- Transferring Instances for Model-Based Reinforcement Learning -- A Simple Model for Sequences of Relational State Descriptions -- Semi-Supervised Boosting for Multi-Class Classification -- A Joint Segmenting and Labeling Approach for Chinese Lexical Analysis -- Transferred Dimensionality Reduction -- Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model -- Estimating Sales Opportunity Using Similarity-Based Methods -- Learning MDP Action Models Via Discrete Mixture Trees -- Continuous Time Bayesian Networks for Host Level Network Intrusion Detection -- Data Streaming with Affinity Propagation -- Semi-supervised Discriminant Analysis Via CCCP -- Demo Papers -- A Visualization-Based Exploratory Technique for Classifier Comparison with Respect to Multiple Metrics and Multiple Domains -- Pleiades: Subspace Clustering and Evaluation -- SEDiL: Software for Edit Distance Learning -- Monitoring Patterns through an Integrated Management and Mining Tool -- A Knowledge-Based Digital Dashboard for Higher Learning Institutions -- SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model. 330 $aThis book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v5212 606 $aArtificial intelligence 606 $aDatabase management 606 $aInformation storage and retrieval systems 606 $aMachine theory 606 $aAlgorithms 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aArtificial Intelligence 606 $aDatabase Management 606 $aInformation Storage and Retrieval 606 $aFormal Languages and Automata Theory 606 $aAlgorithms 606 $aProbability and Statistics in Computer Science 615 0$aArtificial intelligence. 615 0$aDatabase management. 615 0$aInformation storage and retrieval systems. 615 0$aMachine theory. 615 0$aAlgorithms. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 14$aArtificial Intelligence. 615 24$aDatabase Management. 615 24$aInformation Storage and Retrieval. 615 24$aFormal Languages and Automata Theory. 615 24$aAlgorithms. 615 24$aProbability and Statistics in Computer Science. 676 $a006.31 702 $aMorik$b Katharina 702 $aDaelemans$b Walter 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484036603321 996 $aMachine Learning and Knowledge Discovery in Databases$93568347 997 $aUNINA