Communications of the ACM
Learnable and Nonlearnable Visual Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Equivalence of models for polynomial learnability
COLT '88 Proceedings of the first annual workshop on Computational learning theory
Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Learning Boolean Functions in an Infinite Attribute Space
Machine Learning
Learning in the presence of malicious errors
SIAM Journal on Computing
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
An active vision architecture based on iconic representations
Artificial Intelligence - Special volume on computer vision
Additive versus exponentiated gradient updates for linear prediction
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
On learning visual concepts and DNF formulae
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Representation and recognition in vision
Representation and recognition in vision
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation of pattern invariance in brain-like structures
Neural Networks - Special issue on organisation of computation in brain-like systems
A computational model for visual selection
Neural Computation
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
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
On Learning to Recognize 3-D Objects from Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Up Context-Sensitive Text Correction
Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference
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 Learning Approach to Shallow Parsing
A Learning Approach to Shallow Parsing
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Minimizing Binding Errors Using Learned Conjunctive Features
Neural Computation
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Experiments with Projection Learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Representation for Coarse and Fine Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinct Multicolored Region Descriptors for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition by artificial cortical maps
Neural Networks
Minimum Bayes error features for visual recognition
Image and Vision Computing
Learning a locality discriminating projection for classification
Knowledge-Based Systems
Dynamics of facial expression extracted automatically from video
Image and Vision Computing
Online closure-based learning of relational theories
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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A learning account for the problem of object recognition is developed within the probably approximately correct (PAC) model of learnability. The key assumption underlying this work is that objects can be recognized (or discriminated) using simple representations in terms of syntactically simple relations over the raw image. Although the potential number of these simple relations could be huge, only a few of them are actually present in each observed image, and a fairly small number of those observed are relevant to discriminating an object.We show that these properties can be exploited to yield an efficient learning approach in terms of sample and computational complexity within the PAC model. No assumptions are needed on the distribution of the observed objects, and the learning performance is quantified relative to its experience. Most important, the success of learning an object representation is naturally tied to the ability to represent it as a function of some intermediate representations extracted from the image.We evaluate this approach in a large-scale experimental study in which the SNoW learning architecture is used to learn representations for the 100 objects in the Columbia Object Image Library. Experimental results exhibit good generalization and robustness properties of the SNoW-based method relative to other approaches. SNoW's recognition rate degrades more gracefully when the training data contains fewer views, and it shows similar behavior in some preliminary experiments with partially occluded objects.