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1.
Large margin vs. large volume in transductive learning   总被引:2,自引:0,他引:2  
We consider a large volume principle for transductive learning that prioritizes the transductive equivalence classes according to the volume they occupy in hypothesis space. We approximate volume maximization using a geometric interpretation of the hypothesis space. The resulting algorithm is defined via a non-convex optimization problem that can still be solved exactly and efficiently. We provide a bound on the test error of the algorithm and compare it to transductive SVM (TSVM) using 31 datasets.  相似文献
2.
一种直推式多标记文档分类方法   总被引:2,自引:0,他引:2  
真实世界的文档往往同时属于多个类别,因此,利用多标记学习技术进行文档分类是一个重要的研究方向,现有多标记文档分类方法需要利用大量有正确分类标记的文档才能获得好的分类性能,然而,在实际应用中往往只能得到少量的有标记文档作为分类所需的训练文档.出于利用未标记文档的想法,提出一种基于随机游走的直推式多标记文档分类方法,可以利用大量的未标记文档来辅助提高分类性能,实验结果表明,该方法的性能优于现有直推式多标记分类方法CNMF.  相似文献
3.
This paper presents a novel active learning approach for transductive support vector machines with applications to text classification. The concept of the centroid of the support vectors is proposed so that the selective sampling based on measuring the distance from the unlabeled samples to the centroid is feasible and simple to compute. With additional hypothesis, active learning offers better performance with comparison to regular inductive SVMs and transductive SVMs with random sampling,and it is even competitive to transductive SVMs on all available training data. Experimental results prove that our approach is efficient and easy to implement.  相似文献
4.
We study functions with multiple output values, and use active sampling to identify an example for each of the possible output values. Our results for this setting include: (1) Efficient active sampling algorithms for simple geometric concepts, such as intervals on a line and axis parallel boxes. (2) A characterization for the case of binary output value in a transductive setting. (3) An analysis of active sampling with uniform distribution in the plane. (4) An efficient algorithm for the Boolean hypercube when each output value is a monomial. Editors: Hans Ulrich Simon, Gabor Lugosi, Avrim Blum. A preliminary version of this work was presented in the 19th Annual Conference on Learning Theory (COLT), 2006. This work was supported in part by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778, by a grant No. 1079/04 from the Israel Science Foundation, by a grant from BSF and an IBM faculty award. This publication only reflects the authors’ views.  相似文献
5.
This paper addresses the problem of transductive learning of the kernel matrix from a probabilistic perspective. We define the kernel matrix as a Wishart process prior and construct a hierarchical generative model for kernel matrix learning. Specifically, we consider the target kernel matrix as a random matrix following the Wishart distribution with a positive definite parameter matrix and a degree of freedom. This parameter matrix, in turn, has the inverted Wishart distribution (with a positive definite hyperparameter matrix) as its conjugate prior and the degree of freedom is equal to the dimensionality of the feature space induced by the target kernel. Resorting to a missing data problem, we devise an expectation-maximization (EM) algorithm to infer the missing data, parameter matrix and feature dimensionality in a maximum a posteriori (MAP) manner. Using different settings for the target kernel and hyperparameter matrices, our model can be applied to different types of learning problems. In particular, we consider its application in a semi-supervised learning setting and present two classification methods. Classification experiments are reported on some benchmark data sets with encouraging results. In addition, we also devise the EM algorithm for kernel matrix completion. Editor: Philip M. Long  相似文献
6.
直推式支持向量机(TSVM)是在利用有标签样本的同时,考虑无标签样本对分类器的影响,并且结合支持向量机算法,实现一种高效的分类算法。它在包含少量有标签样本的训练集和大量无标签样本的测试集上,具有良好的效果。但是它有算法时间复杂度比较高,需要预先设置正负例比例等不足。通过对原有算法的改进,新算法在时间复杂度上明显下降,同时算法效果没有明显的影响。  相似文献
7.
Personalized transductive learning (PTL) builds a unique local model for classification of individual test samples and is therefore practically neighborhood dependant; i.e. a specific model is built in a subspace spanned by a set of samples adjacent to the test sample. While existing PTL methods usually define the neighborhood by a predefined (dis)similarity measure, this paper introduces a new concept of a knowledgeable neighborhood and a transductive Support Vector Machine (SVM) classification tree (t-SVMT) for PTL. The neighborhood of a test sample is constructed over the classification knowledge modelled by regional SVMs, and a set of such SVMs adjacent to the test sample is systematically aggregated into a t-SVMT. Compared to a regular SVM and other SVMTs, a t-SVMT, by virtue of the aggregation of SVMs, has an inherent superiority in classifying class-imbalanced datasets. The t-SVMT has also solved the over-fitting problem of all previous SVMTs since it aggregates neighborhood knowledge and thus significantly reduces the size of the SVM tree. The properties of the t-SVMT are evaluated through experiments on a synthetic dataset, eight bench-mark cancer diagnosis datasets, as well as a case study of face membership authentication.  相似文献
8.
In kernel discriminant analysis, it is common practice to select the smoothing parameter (bandwidth) based on the training data and use it for classifying all unlabeled observations. But this method of selecting a single scale of smoothing ignores the major issue of model uncertainty. Moreover, in addition to depending on the training sample, a good choice of bandwidth may also depend on the observation to be classified, and a fixed level of smoothing may not work well in all parts of the measurement space. So, instead of using a single smoothing parameter, it may be more useful in practice to study classification results for multiple scales of smoothing and judiciously aggregate them to arrive at the final decision. This paper adopts a Bayesian approach to carry out one such multiscale analysis using a probabilistic framework. This framework also helps us to extend our multiscale method for semi-supervised classification, where, in addition to the training sample, one uses unlabeled test set observations to form the decision rule. Some well-known benchmark data sets are analyzed to show the utility of these proposed methods.  相似文献
9.
态度挖掘是近年来文本挖掘领域的热点课题之一,旨在发现文本中作者的主观态度倾向,为基于舆情的决策过程提供支持。目前已有的态度挖掘算法绝大多数都基于情感词典来识别情感词,在此基础上判别句子或文本的总体态度倾向。然而,手工构造和维护一部完善的情感词典是不现实的。对中文情感词的极性判别问题进行了研究,提出了基于直推式学习的中文情感词极性判别算法。该算法以少量情感词为种子,利用词典中词汇的解释信息,直推出其他词的情感极性。与使用相同情感种子词的解释信息作为训练数据的有监督学习算法相比,直推式学习算法的识别精度提高了20%左右。  相似文献
10.
In object tracking problem, most methods assume brightness constancy or subspace constancy, which are violated in practice. In this paper, the object tracking problem is considered as a transductive learning problem and a robust tracking method is proposed under intrinsic and extrinsic varieties. The object not only fits the object model, but also has the same cluster with the previous objects, which are the labeled data. By constraining the global and local information, the cost function is constructed firstly. The solution for minimizing the cost function can be solved by a simple linear algebra with graph Laplacian. Moreover, a novel graph is constructed over the positive samples and candidate patches, which can simultaneously learn the object's global appearance model and the local intrinsic geometric structure of all the patches. Furthermore, a heuristic positive samples selection scheme is adopted to make the method more effective. The proposed method is tested on different videos, which undergo large pose, expression, illumination and partial occlusion, and compared with state-of-the-art algorithms. Experimental results and comparative studies are provided to demonstrate the efficiency of the proposed method.  相似文献
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