Communications of the ACM
Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning Boolean Functions in an Infinite Attribute Space
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Artificial Intelligence - Special issue on relevance
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computational model for visual selection
Neural Computation
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Recognize 3D Objects with SNoW
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Texture Recognition Using a Non-Parametric Multi-Scale Statistical Model
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Cluster-Based Statistical Model for Object Detection
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A statistical approach to 3d object detection applied to faces and cars
A statistical approach to 3d object detection applied to faces and cars
Minimizing Binding Errors Using Learned Conjunctive Features
Neural Computation
Journal of Cognitive Neuroscience
Frequent-subsequence-based prediction of outer membrane proteins
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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Numerous statistical learning methods have been developed for visual recognition tasks. Few attempts, however, have been made to address theoretical issues, and in particular, study the suitability of different learning algorithms for visual recognition. Large margin classifiers, such as SNoW and SVM, have recently demonstrated their success in object detection and recognition. In this paper, we present a theoretical account of these two learning approaches, and their suitability to visual recognition. Using tools from computational learning theory, we show that the main difference between the generalization bounds of SVM and SNoW depends on the properties of the data. We argue that learning problems in the visual domain have sparseness characteristics and exhibit them by analyzing data taken from face detection experiments. Experimental results exhibit good generalization and robustness properties of the SNoW-based method, and conform to the theoretical analysis.