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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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半监督异常行为监测pdf，提出新的基于半监督学习的行为建模与异常检测方法，采用基于动态时间归整的归一化距离来建立相似矩阵-Semi-supervised monitoring abnormal behavior pdf, propose a new semi-supervised Learning based on behavior modeling and anomaly detection methods, based on Dynamic Time Warping of the norma
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利用谱聚类方法在特
征向量空间中对原始样本数据进行重新表述使得在新表述中同一聚类中的样本能够更好地积聚在一起构建聚类核函数 并进而构造聚类核半监督支持向量机 使样本更好地满足半监督学习必须遵循的聚类假设 -Restated in the new formulation in the same cluster sample be better able to accumulate together to build the clustering of nuclear function and
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随机游走在计算机学科的信息检索领域已经得到了成功的应用，现在正被
越来越多地应用到机器学习和数据挖掘等领域。在此背景下，我们提出图上的
随机游走学习，创造性地将随机游走作为一项基本技术，用于改善传统的有监
督学习，半监督学习和无监督学习中的困难问题-Random walk has been successfully applied in computer science, information retrieval, is now being increasingly applied
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Adaptation in Convolutionally-Coded
MIMO-OFDM Wireless Systems through
Supervised Learning and SNR Ordering
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一种用于multiple graphs的半监督学习-Efficient semi-supervised learning on locally informative multiple graphs
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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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Classification may refer to:
Library classification
Taxonomic classification (see Taxonomy)
Biological classification of organisms
Medical classification
Scientific classification (disambiguation)
Classification (literature)
Supe
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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，目前已广泛应用于药物设计、图像搜索等领域,并已获得很好的效果。在多示例学习中，训练样本是由多个示例组成的包，包是有概念标记的，但示例本身却没有概念标记，学习的目的是预测新包的类别。-Multi-instance learning and supervised learning, unsupervised learning and reinforcement learning tied for the fourth-class le
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Supervised Machine Learning--A Review of Classification Techniques.rar
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matlab语句实现图模型，多用于分析网络用户产品推荐及网络用户潜在社会关系分析模型的建立。-The goal of building systems that can adapt to their environments and learn from
their experience has attracted researchers from many fields , including computer
science, engineering, mathematics, ph
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As significant work was done in machine learning in past two decades, we have limited to recent articles in machine learning. A brief review of machine learning and its application can found in [Duttion and Conroy,1996). The most common supervised le
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The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in
which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used i
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半监督流行学习算法，看后能懂得流行学习算法的基本原理-Popular semi-supervised learning algorithm, after watching can understand the basic principles of popular learning algorithm
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SUPERVISED LEARNING2 NOTES FOR INTELLIGENT SYSTEMS
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Semantic alignment between ontologies is a necessary precondition to establish interoperability
between agents or services using different ontologies.
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Documentation for active learning and Semi supervised Learning two important machine learning algorithm
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《统计学习方法》是计算机及其应用领域的一门重要的学科。《统计学习方法》全面系统地介绍了统计学习的主要方法，特别是监督学习方法，包括感知机、k近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归与最大熵模型、支持向量机、提升方法、EM算法、隐马尔可夫模型和条件随机场等。除第1章概论和最后一章总结外，每章介绍一种方法。叙述从具体问题或实例入手，由浅入深，阐明思路，给出必要的数学推导，便于读者掌握统计学习方法的实质，学会运用。为满足读者进一步学习的需要，书中还介绍了一些相关研究，给出了少量习题，列出了主要参考文
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MATLAB与机器学习，包含机器学习简介，快速入门，应用监督式学习，应用无监督学习(MATLAB and Machine Learning, including Machine Learning Introduction, Quick Start, Application Supervised Learning, Application Unsupervised Learning)
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