Multilayer feedforward networks are universal approximators
Neural Networks
Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A bootstrapping algorithm for learning linear models of object classes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Fingerprint and Speaker Verification Decisions Fusion
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stable Fitting of 2D Curves and 3D Surfaces by Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Neural Networks
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
Anisotropic noise injection for input variables relevance determination
IEEE Transactions on Neural Networks
Learning capability and storage capacity of two-hidden-layer feedforward networks
IEEE Transactions on Neural Networks
An error-counting network for pattern classification
Neurocomputing
Maximizing area under ROC curve for biometric scores fusion
Pattern Recognition
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This paper presents a reciprocal-sigmoid model for pattern classification. This proposed classifier can be considered as a 驴-machine since it preserves the theoretical advantage of linear machines where the weight parameters can be estimated in a single step. The model can also be considered as an approximation to logistic regression under the framework of Generalized Linear Models. While inheriting the necessary classification capability from logistic regression, the problems of local minima and tedious recursive search no longer exist in the proposed formulation. To handle possible over-fitting when using high order models, the classifier is trained using multiple samples of uniformly scaled pattern features. Empirically, the classifier is evaluated using a benchmark synthetic data from random sampling runs for initial statistical evidence regarding its classification accuracy and computational efficiency. Additional experiments based on ten runs of 10-fold cross validations on 40 data sets further support the effectiveness of the reciprocal-sigmoid model, where its classification accuracy is seen to be comparable to several top classifiers in the literature. Main reasons for the good performance are attributed to effective use of reciprocal sigmoid for embedding nonlinearities and effective use of bundled feature sets for smoothing the training error hyper-surface.