This paper presents an active learning strategy for boosting. In this strategy, we construct a novel objective function to unify semi-supervised learning and active learning boosting. Minimization of this objective is...
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ISBN:
(纸本)9781615671090
This paper presents an active learning strategy for boosting. In this strategy, we construct a novel objective function to unify semi-supervised learning and active learning boosting. Minimization of this objective is achieved through alternating optimization with respect to the classifier ensemble and the queried data set iteratively. Previous semi-supervised learning or active learning methods based on boosting can be viewed as special cases under this framework. More important, we derive an efficient active learning algorithm under this framework, based on a novel query mechanism called query by incremental committee. It does not only save considerable computational cost, but also outperforms conventional active learning methods based on boosting. We report the experimental results on both boosting benchmarks and real-world database, which show the efficiency of our algorithm and verify our theoretical analysis.
Motion segmentation is an important topic in computer vision. In this paper, we study the problem of multi-body motion segmentation under the affine camera model. We use a mixture of subspace model to describe the mul...
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Nonnegative Matrix Factorization (NMF) has been widely used in computer vision and pattern recognition. It aims to find two nonnegative matrices whose product can well approximate the nonnegative data matrix, which na...
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Text classification is a very important task in information retrieval and data mining. In vector space model (VSM), document is represented as a high dimensional vector, and a feature extraction phase is usually neede...
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ISBN:
(纸本)9781615671090
Text classification is a very important task in information retrieval and data mining. In vector space model (VSM), document is represented as a high dimensional vector, and a feature extraction phase is usually needed to reduce the dimensionality of the document. In this paper, we propose a feature extraction method, named Local Relevance Weighted Maximum Margin Criterion (LRWMMC). It aims to learn a subspace in which the documents in the same class are as near as possible while the documents in the different classes are as far as possible in the local region of each document. Furthermore, the relevance is taken into account as a weight to determine the extent to which the documents will be projected. LRWMMC is able to find the low dimensional manifold embedded in the high dimensional ambient space. In addition, We generalize LRWMMC to Reproducing Kernel Hubert Space (RKHS), which can resolve the nonlinearity of the input space. We also generalize LRWMMC to tensor space which is suitable for a new document representation, named tensor space model (TSM). On the other hand, in order to utilize the large amount of unlabeled documents, we also present a Semi-Supervised LRWMMC, which aims to find a projection inferred from the labeled samples, as well as the unlabeled samples. Finally, we present a fast algorithm based on QR-decomposition to make the methods proposed in this paper apply for large scale data set. Encouraging experimental results on benchmark text classification data sets indicate that the proposed methods out-perform many existing feature extraction methods for text classification.
Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platform, remains one of the challenge problems despite of numerous studies. This paper presents a novel scheduling algorithm...
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We present a vision-based landing algorithm for an autonomous helicopter under complex environment (there are several suspected targets). The algorithm is integrated with algorithms for visual acquisition, recognition...
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Main difficulty encountered in space teleoperation is a large time delay in communication between earth and space [1]. Predictive control strategy [2] via virtual reality (VR) or augmented reality (AR) is an available...
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A discrete-time jump fuzzy system with two Markov chains is proposed to portray the asymmetric network characteristic of a class of nonlinear NCSs with random but bounded communication delays and packets dropout. The ...
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Approximate near neighbor search plays a critical role in various kinds of multimedia applications. The vocabulary-based hashing scheme uses vocabularies, i.e. selected sets of feature points, to define a hash functio...
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In this paper, we introduce a parallel algorithm to implement the Region Growing algorithms in GPU, with the purpose of 3D organ segmentation. Extensive Experiments have been executed on human CT Data, and these exper...
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