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Semi-supervised Learning by Entropy Minimization
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近三年来半监督学习的国外顶级期刊论文，办监督的最新研究成果,Over the past three years semi-supervised learning of foreign top-level journal articles, to do oversight of the latest research results
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.Sparse coding algorithm.We can also apply it onefficient sparse coding algorithm to a new machine learning framework called "self-taught learning", where we are given a small amount of labeled data for a supervised learning task, and lots of additio
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wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器-university texas austin are wekaUT the development of guidance based on semi-weka study (semi supervised learning) classifier
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Semi-supervised learning
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这系列课件系统地讲述了模式识别的基本理论和基本方法。内容涵盖了贝叶斯决策、概率密度函数的估计、线性判别函数、邻近法则、特征的选择和提取、非监督学习、神经网络、模糊模式识别等。-This series of courseware on a pattern recognition system to the basic theory and basic methods. Covers the Bayesian decision-making, the estimated probability de
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贝叶斯分类和后面的线性、非线性分类器属于有监督学习。
-Bayesian classification and the back of the linear, non-linear classifier belong to supervised learning.
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第一本全面的半监督学习参考书，里面涵盖了半监督方面的各种算法，应用，以及理论证明-This is a comprehensive reference of semi-surpervised learning, including the algrithms, application, and the proof in theory.
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综合了主动学习和半监督学习的多项算法，很有价值的学习资料-Combination of active learning and a number of semi-supervised learning algorithm, learning valuable information
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Introduction to semi-supervised learning
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义了一个欧氏距离和监督信息相混合的新的最近邻计算函数，从而将K一均值算法很好地应用于半
监督聚类问题。针对K一均值算法初始质心敏感的缺陷，用粒子群算法的搜索空间模拟聚类的欧氏空间，迭代搜
索找到较优的聚类质心，同时提出动态管理种群的策略以提高粒子群算法搜索效率。算法在UCI的多个数据集
上测试都得到了较好的聚类准确率。-Righteousness of a Euclidean distance and supervision of a mixture of new nearest n
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多示例学习是与监督学习、非监督学习和强化学习并列的第四类学习框架-Multi-instance learning with supervised learning, unsupervised learning and strengthen the learning parallel learning framework
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A PhD thesis on Semi-supervised learning with Graphs by Xiaojin Zhu. Focuses on creating graphs, based on a mixture of labeled and unlabeled data (as per the semi-supervised learning paradigm) and using processes on these graphs to propagate in rigo
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半监督学习非常好的一篇文章，机器学习重镇，南京大学写的，希望对大家有所帮助！-Semi-supervised learning is very good article, machine learning center, Nanjing University, write, we want to help!
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这是一篇关于半监督的论文，半监督学习问题广泛存在于现实世界中, 已经成为目前机器学习和模式识别领域中的一个研究热点. 文章
综述了半监督学习问题的基本思想、研究现状、常用算法及其一些应用领域, 分析了目前存在的主要困难, 并指出需进一步研究的几个问题.-Sem-i supervised learning has been w idely used in the w orld and become a hot topic in the resear ch
field of machine
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SUPERVISED LEARNING2 NOTES FOR INTELLIGENT SYSTEMS
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经典的书，李航讲的统计学习方法，里面主要涉及监督学习方法，感知机、K邻近法、决策树、支持向量机等-Classic book, statistical learning methods Li Hang speak, which is mainly involved in supervised learning method, Perceptron, K neighboring France, decision trees, support vector machine
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ML supervised Learning
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2-Supervised Learning
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This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a va
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