Add to ORKGRecently, boosting is used widely in object detection ap-plications because of its impressive performance in both speed and accuracy. However, learning weak classifiers which is one of the most significant tasks in using boost-ing is left for users. This paper describes a novel method for efficiently learning weak classifiers using entropy mea-sures, called Ent-Boost. The class entropy information is used to estimate the optimal number of bins automatically through discretization process. Then Kullback-Leibler di-vergence which is the relative entropy between probability distributions of positive and negative samples is employed to select the best weak classifier in the weak classifier set. Experiments have shown that strong classifiers lea...
To obtain classification systems with both good generalization per-formance and efficiency in space ...
This paper presents a new boosting algorithm called NormalBoost which is capable of classifying a mu...
AbstractBoosting algorithms are procedures that “boost” low-accuracy weak learning algorithms to ach...
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary c...
We propose a novel mapping method to improve the train-ing accuracy and efficiency of boosted classi...
using entropy measures for robust object detection Duy-Dinh Le a, *, Shin’ichi Satoh a,
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary c...
Recent work [1], has shown that improving model learning for weak classifiers can yield significant ...
Boosting algorithms are procedures that “boost ” low-accuracy weak learning algorithms to achieve ar...
AbstractWe consider a boosting technique that can be directly applied to multiclass classification p...
Boosting algorithms are procedures that “boost ” low accuracy weak learning algorithms to achieve ar...
AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the t...
From family of corrective boosting algorithms (i.e. AdaBoost, LogitBoost) to total corrective algori...
Abstract — Recent work [1], has shown that improving model learning for weak classifiers can yield s...
AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the tr...
To obtain classification systems with both good generalization per-formance and efficiency in space ...
This paper presents a new boosting algorithm called NormalBoost which is capable of classifying a mu...
AbstractBoosting algorithms are procedures that “boost” low-accuracy weak learning algorithms to ach...
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary c...
We propose a novel mapping method to improve the train-ing accuracy and efficiency of boosted classi...
using entropy measures for robust object detection Duy-Dinh Le a, *, Shin’ichi Satoh a,
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary c...
Recent work [1], has shown that improving model learning for weak classifiers can yield significant ...
Boosting algorithms are procedures that “boost ” low-accuracy weak learning algorithms to achieve ar...
AbstractWe consider a boosting technique that can be directly applied to multiclass classification p...
Boosting algorithms are procedures that “boost ” low accuracy weak learning algorithms to achieve ar...
AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the t...
From family of corrective boosting algorithms (i.e. AdaBoost, LogitBoost) to total corrective algori...
Abstract — Recent work [1], has shown that improving model learning for weak classifiers can yield s...
AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the tr...
To obtain classification systems with both good generalization per-formance and efficiency in space ...
This paper presents a new boosting algorithm called NormalBoost which is capable of classifying a mu...
AbstractBoosting algorithms are procedures that “boost” low-accuracy weak learning algorithms to ach...