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本代码实现基于成对约束的半监督图嵌入算法-Following the intuition that the image variation of
faces can be effectively modeled by low dimensional
linear spaces, we propose a novel linear subspace
learning method for face analysis in the framework of
graph embeddi
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CoForest是一种半监督算法，处理集成学习及利用大量未标记数据得到更优越性能的假设。-CoForest is a semi-supervised algorithm, which exploits the power of ensemble learning and large amount of unlabeled data available to produce hypothesis with better performance.
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进行半监督转倒式训练
通过半监督学习进行分类-for semi-supervised maching learning,the paper is 《Large Scale Transductive SVMs》
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Watermarking embeds information into a digital signal like
audio, image, or video. Reversible image watermarking can restore the
original image without any distortion after the hidden data is extracted.
In this paper, we present a novel reversi
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关于metric learning的综述，涉及到许多的知识：SVM、kernel、SDP等-This paper surveys the field of distance
metric learning from a principle perspective, and includes a broad selection of recent work. In particular, distance metric learning is reviewed under different
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Naive bayes classifer的具体实现，使用多模态事件模型表示，提供EM算法用于半监督和无监督学习，最大似然估计用于有监督学习-The Naive bayes classifer implementation, using a multi-modal event model EM algorithm for semi-supervised and unsupervised learning, maximum likelihood estimation for supervised
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Provable Learning of Overcomplete Latent Variable Models Semi-supervised and Unsupervised Settings
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最新极限学习机程序,绝对可以使用，请大家下线载-G. Huang, S. Song, J. N. D. Gupta, and C. Wu, “Semi-supervised and Unsupervised Extreme Learning Machines,” (in press) IEEE Transactions on Cybernetics, 2014.
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半监督核无监督极限学习机，用于半监督核无监督学习，比传统方法速度略快，且可以直接应用多分类问题-A semi-supervised nuclear unsupervised extreme learning machine, used for a semi-supervised kernel unsupervised learning, slightly faster than the traditional methods, and can direct application classif
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用java实现了co-training，用于少量标注数据来获取大量的标注数据，也在于解决数据不均衡问题(This is an algorithm for aspect-based sentiment analysis using co-training, a semi-supervised machine learning algorithm that partitions the machine learning features into two sufficient and uncorre
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The package includes the Matlab code of COINS, which is designed for learning from multi-label data under the inductive semi-supervised setting by adapting the co-training techniques. Source code as well as running demo are included in the package.
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