Artificial Intelligence: Methodology, Systems, and Applications

16th International Conference, AIMSA 2014, Varna, Bulgaria, September 11-13, 2014, Proceedings

AvGennady Agre,Pascal Hitzler

Häftad, Engelska, 2014

Del 8722 i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2014, held in Varna, Bulgaria in September 2014. The 14 revised full papers and 9 short papers presented were carefully reviewed and selected from 53 submissions. The range of topics is almost equally broad, from traditional areas such as computer vision and natural language processing to emerging areas such as mining the behavior of Web-based communities.

Produktinformation

Utgivningsdatum:2014-09-23
Mått:155 x 235 x 18 mm
Vikt:493 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:302
Förlag:Springer International Publishing AG
ISBN:9783319105536

Utforska kategorier

Artificiell intelligens inom Data och IT

Innehållsförteckning

Learning Probabilistic Semantic Network of Object-oriented Action and Activity.- Semantic-aware Expert Partitioning.- User-Level Opinion Propagation Analysis in Discussion Forum Threads.- Social News Feed Recommender.- Boolean Matrix Factorisation for Collaborative Filtering: An FCA-Based Approach.- Semi-Supervised Image Segmentation.- Analysis of Rumor Spreading in Communities Based on Modified SIR Model in Microblog.- Modeling a System for Decision Support in Snow Avalanche Warning.- Using Balanced Random Forest and Weighted Random Forest.- Applying Language Technologies on Healthcare Patient Records for Better Treatment of Bulgarian Diabetic Patients.- Incrementally Building Partially Path Consistent Qualitative Constraint Networks.- A Qualitative Spatio-Temporal Framework Based on Point Algebra.- Training Datasets Collection and Evaluation of Feature Selection Methods for Web Content Filtering.- Feature Selection by Distributions Contrasting.- Educational Data Mining for Analysis of Students' Solutions.