Artificial Neural Networks in Pattern Recognition, 2014 6th IAPR TC 3 International Workshop, ANNPR 2014, Montreal, QC, Canada, October 6-8, 2014, Proceedings Lecture Notes in Artificial Intelligence Series
Coordonnateurs : El Gayar Neamat, Schwenker Friedhelm, Suen Cheng
A Decorrelation Approach for Pruning of Multilayer Perceptron Networks.- Entity Recognition.- Incremental Feature Selection by Block Addition and Block Deletion Using Least Squares SVRs.- Low-dimensional Data Representation in Data Analysis.- Analyzing dynamic ensemble selection techniques using dissimilarity Analysis.- Hidden Markov Models Based on Generalized Dirichlet Mixtures for Proportional Data Modeling.- Majority-Class aware Support Vector Domain Oversampling for Imbalanced Classification Problems.- Forward and Backward Forecasting Ensembles for the Estimation of Time Series Missing Data.- Dynamic Weighted Fusion of Adaptive Classifier Ensembles Based on Changing Data Streams: Combining Bipartite Graph Matching and Beam Search for Graph Edit Distance Approximation.- Computing Upper and Lower Bounds of Graph Edit Distance in Cubic Time.- Linear contrast classifiers in high-dimensional spaces.- A new multi-class fuzzy support vector machine algorithm.- A reinforcement learning algorithm to train a Tetris playing agent.- Bio-inspired optic ow from event-based neuromorphic sensor input.- Comparative Study of Feature Selection for White Blood Cell End-Shape Recognition for Arabic Handwritten Text Segmentation.- Intelligent Ensemble Systems for Modeling NASDAQ Microstructure: A Comparative Study.- Face Recognition based on Discriminative Dictionary with Multilevel Feature Fusion.- Ensembles in Ubiquitous Healthcare Systems.
Date de parution : 09-2014
Ouvrage de 289 p.
15.5x23.5 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 52,74 €
Ajouter au panierThèmes d’Artificial Neural Networks in Pattern Recognition :
Mots-clés :
classification; feature selection; information extraction; kernel methods; learning algorithms; machine learning; meta-learning; multiple classifier systems; neural networks; optimal network architecture; pattern classification; statistical learning; support vector machines; time series prediction; unsupervised active learning