The nature of statistical learning theory
The nature of statistical learning theory
Pairwise classification and support vector machines
Advances in kernel methods
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Towards a Computational Model for Object Recognition in IT Cortex
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A fast learning algorithm for deep belief nets
Neural Computation
Deep learning via semi-supervised embedding
Proceedings of the 25th international conference on Machine learning
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
Exploring Strategies for Training Deep Neural Networks
The Journal of Machine Learning Research
Neocognitron and the Map Transformation Cascade
Neural Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Handwritten alphanumeric character recognition by the neocognitron
IEEE Transactions on Neural Networks
Noise tolerance in a Neocognitron-like network
Neural Networks
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Image recognition problems are usually difficult to solve using raw pixel data. To improve the recognition it is often needed some form of feature extraction to represent the data in a feature space. We use the output of a biologically inspired model for visual recognition as a feature space. The output of the model is a binary code which is used to train a linear classifier for recognizing handwritten digits using the MNIST and USPS datasets. We evaluate the robustness of the approach to a variable number of training samples and compare its performance on these popular datasets to other published results. We achieve competitive error rates on both datasets while greatly improving relatively to related networks using a linear classifier.