Topic
Boosting (machine learning)
About: Boosting (machine learning) is a research topic. Over the lifetime, 9461 publications have been published within this topic receiving 384177 citations.
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TL;DR: It is shown that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks.
Abstract: In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called “tracking by detection” has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause drift. In this paper, we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.
2,101 citations
01 Jan 2003
TL;DR: This chapter overviews some of the recent work on boosting including analyses of AdaBoost's training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of Ada boost for multiclass classification problems; methods of incorporating human knowledge into boosting; and experimental and applied work using boosting.
Abstract: Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost’s training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems; methods of incorporating human knowledge into boosting; and experimental and applied work using boosting.
1,979 citations
TL;DR: The bias-variance decomposition of the error is provided in this paper, which shows that the success of GASEN may lie in that it can significantly reduce the bias as well as the variance.
1,898 citations
TL;DR: This work describes and analyze an efficient algorithm called RankBoost for combining preferences based on the boosting approach to machine learning, and gives theoretical results describing the algorithm's behavior both on the training data, and on new test data not seen during training.
Abstract: We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences arises in several applications, such as that of combining the results of different search engines, or the "collaborative-filtering" problem of ranking movies for a user based on the movie rankings provided by other users. In this work, we begin by presenting a formal framework for this general problem. We then describe and analyze an efficient algorithm called RankBoost for combining preferences based on the boosting approach to machine learning. We give theoretical results describing the algorithm's behavior both on the training data, and on new test data not seen during training. We also describe an efficient implementation of the algorithm for a particular restricted but common case. We next discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different web search strategies, each of which is a query expansion for a given domain. The second experiment is a collaborative-filtering task for making movie recommendations.
1,889 citations
Proceedings Article•
24 Jul 1998TL;DR: RankBoost as discussed by the authors is an algorithm for combining preferences based on the boosting approach to machine learning, which can be applied to several applications, such as that of combining the results of different search engines, or the "collaborative filtering" problem of ranking movies for a user based on movie rankings provided by other users.
Abstract: We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences arises in several applications, such as that of combining the results of different search engines, or the "collaborative-filtering" problem of ranking movies for a user based on the movie rankings provided by other users. In this work, we begin by presenting a formal framework for this general problem. We then describe and analyze an efficient algorithm called RankBoost for combining preferences based on the boosting approach to machine learning. We give theoretical results describing the algorithm's behavior both on the training data, and on new test data not seen during training. We also describe an efficient implementation of the algorithm for a particular restricted but common case. We next discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different web search strategies, each of which is a query expansion for a given domain. The second experiment is a collaborative-filtering task for making movie recommendations.
1,888 citations