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Semi-supervised Learning by Entropy Minimization
- Semi-supervised Learning by Entropy Minimization
基于贝叶斯网络的半监督聚类集成模型
- 已有的聚类集算法基本上都是非监督聚类集成算法,这样不能利用已知信息,使得聚类集成的准确性、鲁棒性和稳定性降低.把半监督学习和聚类集成结合起来,设计半监督聚类集成模型来克服这些缺点.主要工作包括:第一,设计了基于贝叶斯网络的半监督聚类集成(semi-supervised cluster ensemble,简称SCE)模型,并对模型用变分法进行了推理求解;第二,在此基础上,给出了EM(expectation maximization)框架下的具体算法;第三,从UCI(University of Ca
semi01.rar
- 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
AerialImageClassificationMethodBasedonFractalTheor
- 提出一种基于分形理论和BP 神经网络的航空遥感图像有监督分类方法。该方法尝试将航空图像 的光谱信息和纹理特征相结合。它首先将彩色航空图像由RGB 格式转化为HSI 格式,然后,根据亮度计算分 数维、多重分形广义维数谱q-D( q) 和“空隙”等基于分形的纹理特征,同时加入归一化的色度和饱和度作为光 谱特征,采用BP 神经网络作为分类器。通过对彩色航空图像的分类实验,结果证实该方法行之有效。-Based on fractal theory and BP neural network
nk2
- 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
F-0358
- Semi-Supervised algorithm based on Fuzzy C-Means
polsarpro_lecturecourse
- 雷达极化分类软件使用指导书，可以用于监督分类非监督分类。-Radar Polarimetric classification of software instructions can be used for unsupervised classification of the supervised classification.
Support-vector-machine-
- 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
based-on-random-walk
- 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
MIMO-OFDM-Adaptation
- Adaptation in Convolutionally-Coded MIMO-OFDM Wireless Systems through Supervised Learning and SNR Ordering
ssl-on-locally--multiple-graphs
- 一种用于multiple graphs的半监督学习-Efficient semi-supervised learning on locally informative multiple graphs
semi-supervized-learning
- 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
lunakoy
- Classification may refer to: Library classification Taxonomic classification (see Taxonomy) Biological classification of organisms Medical classification Scientific classification (disambiguation) Classification (literature) Supe
image-study
- 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
Classificationofhyper_magebasedonBEMD
- Abstract : As a powerful tool for image processing ，bi-dimensional empirical mode decomposition ( BEMD) covers a wide range of applications． In this paper ，we explore a novel hyperspectral classification algorithm which integrates BEMD and s
Supervised-Machine-Learning--A-Review-of-Classifi
- Supervised Machine Learning--A Review of Classification Techniques.rar
Introduction-to-Graphical-Model
- 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
Tengfei-Shen
- 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
Using-Semi-supervised-Classifiers-for-Credit-Scor
- Using Semi-supervised Classifiers for Credit Scoring
topic-82-supervised-learning
- SUPERVISED LEARNING2 NOTES FOR INTELLIGENT SYSTEMS