01271nam--2200373---450-99000590858020331620131115140117.0978-88-6611-275-4000590858USA01000590858(ALEPH)000590858USA0100059085820131115d2013----km-y0itay50------baitaIT||||||||001yy<<La>> Legenda volgare di santa Chiara da Montefalco nel codice Casanatense 1819Antonella Dejureprefazione di Ugo VignuzziBariCacucci2013XIII, 397 p.24 cmStudi neo-latinicollana diretta da Francesco Tateo12001Studi neo-latinicollana diretta da Francesco Tateo1Chiara : da Montefalco <santa>BNCF808.80382DEJURE,Antonella618157VIGNUZZI,UgoITsalbcISBD990005908580203316VI.3.B. 4536242433 L.M.VI.3.B.00343372BKUMAPASSARO9020131115USA011358PASSARO9020131115USA011401Legenda volgare di santa Chiara da Montefalco nel codice Casanatense 18191073353UNISA06080nam 22008175 450 991048514570332120251226195520.03-319-11812-910.1007/978-3-319-11812-3(CKB)3710000000249783(SSID)ssj0001354104(PQKBManifestationID)11758987(PQKBTitleCode)TC0001354104(PQKBWorkID)11322206(PQKB)10451162(DE-He213)978-3-319-11812-3(MiAaPQ)EBC6296470(MiAaPQ)EBC5594354(Au-PeEL)EBL5594354(OCoLC)892732154(PPN)181352214(EXLCZ)99371000000024978320140927d2014 u| 0engurnn#008mamaatxtccrDiscovery Science 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings /edited by Sašo Džeroski, Panče Panov, Dragi Kocev, Ljupčo Todorovski1st ed. 2014.Cham :Springer International Publishing :Imprint: Springer,2014.1 online resource (XXII, 364 p. 111 illus.)Lecture Notes in Artificial Intelligence,2945-9141 ;8777Bibliographic Level Mode of Issuance: Monograph3-319-11811-0 Includes bibliographical references and index.Explaining Mixture Models through Semantic Pattern Mining and Banded Matrix Visualization -- Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market -- Synthetic Sequence Generator for Recommender Systems – Memory Biased Random Walk on a Sequence Multilayer Network -- Predicting Sepsis Severity from Limited Temporal Observations -- Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining -- Antipattern Discovery in Ethiopian Bagana Songs -- Categorize, Cluster, and Classify: A 3-C Strategy for Scientific Discovery in the Medical Informatics Platform of the Human Brain Project -- Multilayer Clustering: A Discovery Experiment on Country Level Trading Data -- Medical Document Mining Combining Image Exploration and Text Characterization -- Mining Cohesive Itemsets in Graphs -- Mining Rank Data -- Link Prediction on the Semantic MEDLINE Network: An Approach to Literature-Based Discovery -- Medical Image Retrieval Using Multimodal Data -- Fast Computation of the Tree Edit Distance between Unordered Trees Using IP Solvers -- Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency -- Incremental Learning with Social Media Data to Predict Near Real-Time Events -- Stacking Label Features for Learning Multilabel Rules -- Selective Forgetting for Incremental Matrix Factorization in Recommender Systems -- Providing Concise Database Covers Instantly by Recursive Tile Sampling -- Resampling-Based Framework for Estimating Node Centrality of Large Social Network -- Detecting Maximum k-Plex with Iterative Proper l-Plex Search -- Exploiting Bhattacharyya Similarity Measure to Diminish User Cold-Start Problem in Sparse Data -- Failure Prediction – An Application in the Railway Industry -- Wind Power Forecasting Using Time Series Cluster Analysis -- Feature Selection in Hierarchical Feature Spaces -- Incorporating Regime Metrics into Latent Variable Dynamic Models to Detect Early-Warning Signals of Functional Changes inFisheries Ecology -- An Efficient Algorithm for Enumerating Chordless Cycles and Chordless Paths -- Algorithm Selection on Data Streams -- Sparse Coding for Key Node Selection over Networks -- Variational Dependent Multi-output Gaussian Process Dynamical Systems.This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2014, held in Bled, Slovenia, in October 2014. The 30 full papers included in this volume were carefully reviewed and selected from 62 submissions. The papers cover topics such as: computational scientific discovery; data mining and knowledge discovery; machine learning and statistical methods; computational creativity; mining scientific data; data and knowledge visualization; knowledge discovery from scientific literature; mining text, unstructured and multimedia data; mining structured and relational data; mining temporal and spatial data; mining data streams; network analysis; discovery informatics; discovery and experimental workflows; knowledge capture and scientific ontologies; data and knowledge integration; logic and philosophy of scientific discovery; and applications of computational methods in various scientific domains.Lecture Notes in Artificial Intelligence,2945-9141 ;8777Artificial intelligenceData miningInformation storage and retrieval systemsDatabase managementAlgorithmsArtificial IntelligenceData Mining and Knowledge DiscoveryInformation Storage and RetrievalDatabase ManagementAlgorithmsArtificial intelligence.Data mining.Information storage and retrieval systems.Database management.Algorithms.Artificial Intelligence.Data Mining and Knowledge Discovery.Information Storage and Retrieval.Database Management.Algorithms.006.3Džeroski Sašoedthttp://id.loc.gov/vocabulary/relators/edtPanov Pančeedthttp://id.loc.gov/vocabulary/relators/edtKocev Dragiedthttp://id.loc.gov/vocabulary/relators/edtTodorovski Ljupčoedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910485145703321Discovery Science2968615UNINA