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Advances in Knowledge Discovery and Data Mining 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I

Aus der Reihe Lecture Notes in Artificial Intelligence

49,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.03.2013

Abbildungen

XXII, 199 illus., schwarz-weiss Illustrationen

Herausgeber

Jian Pei + weitere

Verlag

Springer Berlin

Seitenzahl

610

Maße (L/B/H)

23,5/15,5/3,4 cm

Gewicht

943 g

Auflage

2013

Sprache

Englisch

ISBN

978-3-642-37452-4

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.03.2013

Abbildungen

XXII, 199 illus., schwarz-weiss Illustrationen

Herausgeber

Verlag

Springer Berlin

Seitenzahl

610

Maße (L/B/H)

23,5/15,5/3,4 cm

Gewicht

943 g

Auflage

2013

Sprache

Englisch

ISBN

978-3-642-37452-4

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: [email protected]

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  • Produktbild: Advances in Knowledge Discovery and Data Mining
  • Produktbild: Advances in Knowledge Discovery and Data Mining
  • Discovering Local Subgroups, with an Application to Fraud Detection.- PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining of Uncertain Data.- Frequent Pattern Mining in Attributed Trees.- Mining Frequent Patterns from Human Interactions in Meetings Using Directed Acyclic Graphs.- ClaSP: An Efficient Algorithm for Mining Frequent Closed Sequences.- Efficient Mining of Contrast Patterns on Large Scale Imbalanced Real-Life Data.- Online Cross-Lingual PLSI for Evolutionary Theme Patterns Analysis.- F-Trail: Finding Patterns in Taxi Trajectories.- Mining Appliance Usage Patterns in Smart Home Environment.- Computational Models of Stress in Reading Using Physiological and Physical Sensor Data.- Latent Patient Profile Modelling and Applications with Mixed-Variate Restricted Boltzmann Machine.- MassBayes: A New Generative Classifier with Multi-dimensional Likelihood Estimation.- Fast and Effective Single Pass Bayesian Learning.- Sparse Reductions for Fixed-Size Least Squares Support Vector Machines on Large Scale Data.- Discovery of Regional Co-location Patterns with k-Nearest Neighbor Graph.- Spectral Decomposition for Optimal Graph Index Prediction.- Patterns amongst Competing Task Frequencies: Super-Linearities, and the Almond-DG Model.- Node Classification in Social Network via a Factor Graph Model.- Fast Graph Stream Classification Using Discriminative Clique Hashing.- Mining Interesting Itemsets in Graph Datasets.- Robust Synchronization-Based Graph Clustering.- Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors.- Exploiting Temporal Information in a Two-Stage Classification Framework for Content-Based Depression Detection.- EEG-Based Person Verification Using Multi-Sphere SVDD and UBM.- Measuring Reproducibility of High-Throughput Deep-Sequencing Experiments Based on Self-adaptive Mixture Copula.- Mining Representative Movement Patterns through Compression.- NARGES: Prediction Model for Informed Routing in a Communications Network.- Mining Usage Traces of Mobile Apps for Dynamic Preference Prediction.- Leveraging Hybrid Citation Context for Impact Summarization.- Optimal Allocation of High Dimensional Assets through Canonical Vines.- Inducing Context Gazetteers from Encyclopedic Databases for Named Entity Recognition.- An Optimization Method for Proportionally Diversifying Search Results.- Joint Naıve Bayes and LDA for Unsupervised Sentiment Analysis.- An Unsupervised Learning Model to Perform Side Channel Attack.- Decisive Supervised Learning.- Learning Overlap Optimization for Domain Decomposition Methods.- CLUEKR : CLUstering Based Efficient kNN Regression.- AREM: A Novel Associative Regression Model Based on EM Algorithm.- One-Class Transfer Learning with Uncertain Data.- Time Series Forecasting Using Distribution Enhanced Linear Regression.- Twin Bridge Transfer Learning for Sparse Collaborative Filtering.- Dimensionality Reduction with Dimension Selection.- Multi-View Visual Classification via a Mixed-Norm Regularizer.- Mining Specific Features for Acquiring User Information Needs.- Ensemble-Based Wrapper Methods for Feature Selection and Class Imbalance Learning.- Exploring Groups from Heterogeneous Data via Sparse Learning.- Multiplex Topic Models.- Integrating Clustering and Ranking on Hybrid Heterogeneous Information Network.- Learning from Multiple Observers with Unknown Expertise.