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
Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
TemporalBoost for Event Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Optical Flow Based Detection in Mixed Human Robot Environments
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Boosting with temporal consistent learners: an application to human activity recognition
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SpatialBoost: adding spatial reasoning to adaboost
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Extracting motion features for visual human activity representation
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents a novel approach to the weak classifier selection based on the GentleBoost framework, based on sharing a set of features at each round. We explore the use of linear dimensionality reduction methods to guide the search for features that share some properties, such as correlations and discriminative properties. We add this feature set as a new parameter of the decision stump, which turns the single branch selection of the classic stump into a fuzzy decision that weights the contribution of both branches. The weights of each branch act as a confidence measure based on the feature set characteristics, which increases the accuracy and robustness to data perturbations. We propose an algorithm that consider the similarities between the weights provided by three linear mapping algorithms: PCA, LDA and MMLMNN [14]. We propose to analyze the row vectors of the linear mapping, grouping vector components with very similar values. Then, the created groups are the inputs of the FuzzyBoost algorithm. This search procedure generalizes the previous temporal FuzzyBoost [10] to any type of features. We present results in features with spatial support (images) and spatiotemporal support (videos), showing the generalization properties of the FuzzyBoost algorithm in other scenarios