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| Titolo: |
Artificial Neural Networks in Pattern Recognition : Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006, Proceedings / / edited by Friedhelm Schwenker, Simone Marinai
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| Pubblicazione: | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
| Edizione: | 1st ed. 2006. |
| Descrizione fisica: | 1 online resource (X, 302 p.) |
| Disciplina: | 006.4 |
| Soggetto topico: | Artificial intelligence |
| Pattern recognition systems | |
| Application software | |
| Computer science | |
| Electronic data processing - Management | |
| Bioinformatics | |
| Artificial Intelligence | |
| Automated Pattern Recognition | |
| Computer and Information Systems Applications | |
| Theory of Computation | |
| IT Operations | |
| Altri autori: |
SchwenkerFriedhelm
MarinaiSimone
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| Note generali: | "The second IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2006, was held at the University of Ulm (Germany)"--Pref. |
| Nota di bibliografia: | Includes bibliographical references and index. |
| Nota di contenuto: | Unsupervised Learning -- Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions -- Comparison Between Two Spatio-Temporal Organization Maps for Speech Recognition -- Adaptive Feedback Inhibition Improves Pattern Discrimination Learning -- Semi-supervised Learning -- Supervised Batch Neural Gas -- Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes -- On the Effects of Constraints in Semi-supervised Hierarchical Clustering -- A Study of the Robustness of KNN Classifiers Trained Using Soft Labels -- Supervised Learning -- An Experimental Study on Training Radial Basis Functions by Gradient Descent -- A Local Tangent Space Alignment Based Transductive Classification Algorithm -- Incremental Manifold Learning Via Tangent Space Alignment -- A Convolutional Neural Network Tolerant of Synaptic Faults for Low-Power Analog Hardware -- Ammonium Estimation in a Biological Wastewater Plant Using Feedforward Neural Networks -- Support Vector Learning -- Support Vector Regression Using Mahalanobis Kernels -- Incremental Training of Support Vector Machines Using Truncated Hypercones -- Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques -- Multiple Classifier Systems -- Multiple Classifier Systems for Embedded String Patterns -- Multiple Neural Networks for Facial Feature Localization in Orientation-Free Face Images -- Hierarchical Neural Networks Utilising Dempster-Shafer Evidence Theory -- Combining MF Networks: A Comparison Among Statistical Methods and Stacked Generalization -- Visual Object Recognition -- Object Detection and Feature Base Learning with Sparse Convolutional Neural Networks -- Visual Classification of Images by Learning Geometric Appearances Through Boosting -- An Eye Detection System Based on Neural Autoassociators -- Orientation Histograms for Face Recognition -- Data Mining in Bioinformatics -- An Empirical Comparison of Feature Reduction Methods in the Context of Microarray Data Classification -- Unsupervised Feature Selection for Biomarker Identification in Chromatography and Gene Expression Data -- Learning and Feature Selection Using the Set Covering Machine with Data-Dependent Rays on Gene Expression Profiles. |
| Sommario/riassunto: | This book constitutes the refereed proceedings of the Second IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2006, held in Ulm, Germany in August/September 2006. The 26 revised papers presented were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on unsupervised learning, semi-supervised learning, supervised learning, support vector learning, multiple classifier systems, visual object recognition, and data mining in bioinformatics. |
| Titolo autorizzato: | Artificial neural networks in pattern recognition ![]() |
| ISBN: | 3-540-37952-5 |
| Formato: | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910484426103321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |