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Description: Joachims的SVM-light工具包 里面含有 两个.exe文件svm_classify.exe svm_learn.exe 可以实现Transductive SVM用Anton s Matlab interface to SVM light 使用会更方便-Joachims s SVM-light kit which contains two. Exe files svm_classify.exe svm_learn.exe can Transductive SVM with Anton s Matlab interface to SVM light will be more convenient to use
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Size: 1937408 |
Author: Mingjie Qian |
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Description: 对直推式支持向量机的较为经典的介绍，包含一些直推式学习的思想，算法-Direct Push on Support Vector Machine, introduced a more classic, contains a number of Direct Push the idea of learning, algorithm
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Size: 122880 |
Author: jz |
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Description: svm matlab工具箱，经过测试，非常好用！有界面。-svm matlab toolbox, tested, very good! Interface there is.
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Size: 1072128 |
Author: 李力 |
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Description: 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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Size: 155648 |
Author: Iris |
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Description: 利用基于图的分类方法， 半监督学习 ，分类软件。-SemiL is efficient software for solving large scale semi-supervised learning or transductive inference problems using graph based approaches.
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Size: 1540096 |
Author: lazi |
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Description: Transductive Support Vector Classification for RNA Related Biological Abstracts
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Size: 7168 |
Author: nadeem |
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Description: TRAM程序使用MATLAB代码编写实现直推式多标记学习算法。里面包括可读文件和实例。-This package includes the MATLAB codes for transductive multi-label learning algorithm. A Readme file and an example file are included in the package.
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Size: 1972224 |
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Description: SVM light 工具箱 包含window版本和matlab版本
由美国cornell大学的教授Thorsten Joachims部署
执行SVM二分类
速度明显快于libsvm
下载文件中包含
1.例子(inductive SVM 和 transductive SVM)
2.说明文件
3.源程序-SVM light kit (including window version and matlab version)
Cornell university professors from the United States to deploy Thorsten Joachims
Executive SVM binary classification
Significantly faster than libsvm
Download file contains
1 example (inductive SVM and transductive SVM)
(2) Documentation
3 source
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Size: 4265984 |
Author: vipqyk |
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Description: 该代码包实现了直推学习算法(transductive learning),该算法基于kNN的一个扩展。优点是不需要任何启发式诱导，可以防止不收敛或者局部收敛-We present a new method for transductive
learning, which can be seen as a transductive
version of the k nearest-neighbor classifier.
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Size: 10981376 |
Author: john |
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Description: In the distributed processing, where common labeled data may be not
available for designing classifier ensemble, however, an ensemble solution is
necessary, traditional fixed decision aggregation could not account for class prior
mismatch or classifier dependencies in electronic technology. Previous
transductive learning strategies have several drawbacks, e.g., feasibility of the
constraints was not guaranteed and heuristic learning was applied. We overcome
these problems by developing improved iterative scaling (IIS) algorithm for
optimal solution. This method is shown to achieve improved decision accuracy
over the earlier approaches in electronic technology
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Size: 157696 |
Author: yangs |
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Description: Hyperspectral image classification with limited
number of labeled pixels is a challenging task. In this paper, we
propose a bilayer graph-based learning framework to address
this problem. For graph-based classification, how to establish
the neighboring relationship among the pixels the high
dimensional features is the key toward a successful classification.
Our graph learning algorithm contains two layers. The first-layer
constructs a simple graph, where each vertex denotes one pixel
and the edge weight encodes the similarity between two pixels.
Unsupervised learning is then conducted to estimate the grouping
relations among different pixels. These relations are subsequently
fed into the second layer to form a hypergraph structure, on top
of which, semisupervised transductive learning is conducted to
obtain the final classification results. Our experiments on three
data sets demonstrate the merits of our proposed approach,
which compares favorably with state of the art.-Hyperspectral image classification with limited
number of labeled pixels is a challenging task. In this paper, we
propose a bilayer graph-based learning framework to address
this problem. For graph-based classification, how to establish
the neighboring relationship among the pixels the high
dimensional features is the key toward a successful classification.
Our graph learning algorithm contains two layers. The first-layer
constructs a simple graph, where each vertex denotes one pixel
and the edge weight encodes the similarity between two pixels.
Unsupervised learning is then conducted to estimate the grouping
relations among different pixels. These relations are subsequently
fed into the second layer to form a hypergraph structure, on top
of which, semisupervised transductive learning is conducted to
obtain the final classification results. Our experiments on three
data sets demonstrate the merits of our proposed approach,
which compares favorably with state of the art.
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Size: 2847744 |
Author: bala |
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Description: 迁移学习 领域适应性 机器学习 学习代码(Transfer learning Domain Adaptation Machine Learning Coding study Inductive Learning Transductive Learning)
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Size: 16665600 |
Author: 叮当xy
|
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Description: TSVM直推式支持向量机 TSVM实现直推向量机 设置初始参数 设置核函数类型 将核类型转换为数值参数(transductive-SVM switch to numerical parameter for kernel for RBF kernel change sigma to gamma)
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Size: 1024 |
Author: 寂静之声boy |
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