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[null,null,"Titolo",null,"Machine learning and knowledge discovery in databases. : European conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008 : proceedings, Part 1 / / Walter Daelemans, Bart Goethals, Katharina Morik, (editors)"]
[null,null,"Pubbl/distr/stampa",null,"Berlin ; ; Heidelberg : , : Springer, , [2008]"]
[{"$nulls":4},"Â©2008"]
[null,null,"ISBN",null,"3-540-87479-8"]
[null,null,"Edizione",null,"[1st ed. 2008.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XXIV, 692 p.)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence ; ; 5211"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Machine learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Invited Talks (Abstracts) -- Industrializing Data Mining, Challenges and Perspectives -- From Microscopy Images to Models of Cellular Processes -- Data Clustering: 50 Years Beyond K-means -- Learning Language from Its Perceptual Context -- The Role of Hierarchies in Exploratory Data Mining -- Machine Learning Journal Abstracts -- Rollout Sampling Approximate Policy Iteration -- New Closed-Form Bounds on the Partition Function -- Large Margin vs. Large Volume in Transductive Learning -- Incremental Exemplar Learning Schemes for Classification on Embedded Devices -- A Collaborative Filtering Framework Based on Both Local User Similarity and Global User Similarity -- A Critical Analysis of Variants of the AUC -- Improving Maximum Margin Matrix Factorization -- Data Mining and Knowledge Discovery Journal Abstracts -- Finding Reliable Subgraphs from Large Probabilistic Graphs -- A Space Efficient Solution to the Frequent String Mining Problem for Many Databases -- The Boolean Column and Column-Row Matrix Decompositions -- SkyGraph: An Algorithm for Important Subgraph Discovery in Relational Graphs -- Mining Conjunctive Sequential Patterns -- Adequate Condensed Representations of Patterns -- Two Heads Better Than One: Pattern Discovery in Time-Evolving Multi-aspect Data -- Regular Papers -- TOPTMH: Topology Predictor for Transmembrane ?-Helices -- Learning to Predict One or More Ranks in Ordinal Regression Tasks -- Cascade "]
[{"$nulls":4},"RSVM in Peer-to-Peer Networks -- An Algorithm for Transfer Learning in a Heterogeneous Environment -- Minimum-Size Bases of Association Rules -- Combining Classifiers through Triplet-Based Belief Functions -- An Improved Multi-task Learning Approach with Applications in Medical Diagnosis -- Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis -- Sequence Labelling SVMs Trained in One Pass -- Semi-supervised Classification from Discriminative Random Walks -- Learning Bidirectional Similarity for Collaborative Filtering -- Bootstrapping Information Extraction from Semi-structured Web Pages -- Online Multiagent Learning against Memory Bounded Adversaries -- Scalable Feature Selection for Multi-class Problems -- Learning Decision Trees for Unbalanced Data -- Credal Model Averaging: An Extension of Bayesian Model Averaging to Imprecise Probabilities -- A Fast Method for Training Linear SVM in the Primal -- On the Equivalence of the SMO and MDM Algorithms for SVM Training -- Nearest Neighbour Classification with Monotonicity Constraints -- Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer -- Mining Edge-Weighted Call Graphs to Localise Software Bugs -- Hierarchical Distance-Based Conceptual Clustering -- Mining Frequent Connected Subgraphs Reducing the Number of Candidates -- Unsupervised Riemannian Clustering of Probability Density Functions -- Online Manifold Regularization: A New Learning Setting and Empirical Study -- A Fast Algorithm to Find Overlapping Communities in Networks -- A Case Study in Sequential Pattern Mining for IT-Operational Risk -- Tight Optimistic Estimates for Fast Subgroup Discovery -- Watch, Listen & Learn: Co-training on Captioned Images and Videos -- Parameter Learning in Probabilistic Databases: A Least Squares Approach -- Improving k-Nearest Neighbour Classification with Distance Functions Based on Receiver Operating Characteristics -- One-Class Classification by Combining Density and Class Probability Estimation -- Efficient Frequent Connected Subgraph Mining in Graphs of Bounded Treewidth -- Proper Model Selection with Significance Test -- A Projection-Based Framework for Classifier Performance Evaluation -- Distortion-Free Nonlinear Dimensionality Reduction -- Learning with L q? vs L 1-Norm Regularisation with Exponentially Many Irrelevant Features -- Catenary Support Vector Machines -- Exact and Approximate Inference for Annotating Graphs with Structural SVMs -- Extracting Semantic Networks from Text Via Relational Clustering -- Ranking the Uniformity of Interval Pairs -- Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs -- StreamKrimp: Detecting Change in Data Streams."]
[null,null,null,"Sommario/riassunto","This 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."]