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Recently proposed boosting-based optimization offers a generic framework for the discovery of portfolios of complementary base trading strategies with stable combined performance over wide range of market regimes and robust generalization abilities. However, wide variety of market regimes and existence of hard-to-model periods reduces universe of financial instruments and achievable performance ranges...
Learning to rank is an important area at the interface of machine learning, information retrieval and Web search. The central challenge in optimizing various measures of ranking loss is that the objectives tend to be non-convex and discontinuous. To make such functions amenable to gradient based optimization procedures one needs to design clever bounds. In recent years, boosting, neural networks,...
We investigate AdaBoost and bipartite version of RankBoost abilities to minimize AUC and its application for score level fusion in multimodal biometric systems. To do this, we customize two methods of weak learner training. Empirical results show comparable AUC for AdaBoost and RankBoost.B which previously was addressed theoretically. We demonstrate exhaustive results among state of the art classifiers...
Random Forests (RFs) have become commonplace in many computer vision applications. Their popularity is mainly driven by their high computational efficiency during both training and evaluation while still being able to achieve state-of-the-art accuracy. This work extends the usage of Random Forests to Semi-Supervised Learning (SSL) problems. We show that traditional decision trees are optimizing multi-class...
A noticeable amount of research has been focused on biometric fusion. A new area is looking at utilization of AdaBoost-type learning methods in biometric fusion domain. These methods rely on an idea that by selecting a variety of biometric classifiers the error rate can be reduced. This paper presents a new evolutionary algorithm based on the multi-objective genetic approach, which automatically preserves...
In this paper, we propose a novel robust action recognition framework with the following capabilities: 1) online encoding motions to multi-label sequence where the output in each frame is a tuple of labels rather than a single label, 2) providing efficient automatic relevant motion selection framework, 3) learning systems so as to be optimal for online multi-label sequence classification. As for multi-label...
In this paper, we optimize the boosted results of AdaBoost algorithm by particle swarm optimization (PSO) and form a learning algorithm PSO-AB. We use it to boost the classification ability of support vector machine (SVM) and back propagation neural network (BPNN). This PSO-AB adopts SVM and BPNN to classify the experimental data, uses AdaBoost algorithm to boost the classification results, and then...
AdaBoost was proposed as an efficient algorithm of the ensemble learning field, it selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, redundancy can not be avoided. We proposed a post optimization procedure for the found classifiers and their coefficients based...
LPBoost seemingly should have better generalization capability than AdaBoost according to the margin theory (Schapire, 1999) because LPBoost optimizes the minimum margin directly. Thus far, however, there is no empirical comparison and theoretical explanation of LPBoost against AdaBoost. We have conducted an experimental evaluation on the classification performance of LPBoost and AdaBoost in this...
An example-based classification algorithm to improve generalization performance for detecting objects in images is presented. The classifier integrates component-based classifiers according to the AdaBoost algorithm. A probability estimate by a kernel-SVM is used for the outputs of base learners, which are independently trained for local features. The base learners are determined by selecting the...
Adaboost is an ensemble learning algorithm that combines many base-classifiers to improve their performance. Starting with Viola and Jonespsila researches, Adaboost has often been used to local feature selection for object detection. Adaboost by Viola-Jones consists of following two optimization schemes: (1) training of the local features to make base-classifiers, and (2) selection of the best local...
Many vision problems can be cast as optimizing the conditional probability density function p(C\I) where I is an image and C is a vector of model parameters describing the image. Ideally, the density function p(C\I) would be smooth and unimodal allowing local optimization techniques, such as gradient descent or simplex, to converge to an optimal solution quickly, while preserving significant nonlinearities...
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