EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Self-Organizing Graphs - A Neural Network Perspective of Graph Layout
GD '98 Proceedings of the 6th International Symposium on Graph Drawing
Nonparametric Supervised Learning by Linear Interpolation with Maximum Entropy
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Enhancing Grids for Massively Multiplayer Online Computer Games
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
E-Means: An Evolutionary Clustering Algorithm
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Latent classification models for binary data
Pattern Recognition
Hybrid sampling for imbalanced data
Integrated Computer-Aided Engineering - Selected papers from the IEEE Conference on Information Reuse and Integration (IRI), July 13-15, 2008
On similarities between inference in game theory and machine learning
Journal of Artificial Intelligence Research
Neural network output optimization using interval analysis
IEEE Transactions on Neural Networks
Two-phase construction of multilayer perceptrons using information theory
IEEE Transactions on Neural Networks
Probabilistic classification vector machines
IEEE Transactions on Neural Networks
Performance prediction for RNA design using parametric and non-parametric regression models
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Facial expression biometrics using statistical shape models
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
A non-linear index to evaluate a journal's scientific impact
Information Sciences: an International Journal
Classification Methods with Reject Option Based on Convex Risk Minimization
The Journal of Machine Learning Research
Selecting diversifying heuristics for cluster ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
A genetic procedure used to train RFB neural networks
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Training of RFB neural networks using a full-genetic approach
WSEAS Transactions on Information Science and Applications
A first approach to solve classification problems based on functional networks
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
A robust learning model for dealing with missing values in many-core architectures
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Fuzzy quartile encoding as a preprocessing method for biomedical pattern classification
Theoretical Computer Science
Identification of Relevant Properties for Epitopes Detection Using a Regression Model
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Surrogate data: A novel approach to object detection
International Journal of Applied Mathematics and Computer Science
Proceedings of the 2nd Conference on Wireless Health
Ensemble learning for generalised eigenvalues proximal support vector machines
International Journal of Computer Applications in Technology
Journal of Computer and System Sciences
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
Artificial Intelligence in Medicine
Hierarchical Social Network Analysis Using a Multi-Agent System: A School System Case
International Journal of Agent Technologies and Systems
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This book uses tools from statistical decision theory and computational learning theory to create a rigorous foundation for the theory of neural networks. On the theoretical side, Pattern Recognition and Neural Networks emphasizes probability and statistics. Almost all the results have proofs that are often original. On the application side, the emphasis is on pattern recognition. Most of the examples are from real world problems. In addition to the more common types of networks, the book has chapters on decision trees and belief networks from the machine-learning field. This book is intended for use in graduate courses that teach statistics and engineering. A strong background in statistics is needed to fully appreciate the theoretical developments and proofs. However, undergraduate-level linear algebra, calculus, and probability knowledge is sufficient to follow the book.