搜索资源列表
co-training
- 半监督学习co-training 回归算法的java代码实现。-COREG is a co-training style semi-supervised regression algorithm, which employs two kNN regressors using different distance metrics to select the most confidently labeled unlabeled examples for each other.
sparse_coding
- .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
coforest
- 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.
s4vm
- 该软件包包括半监督算法S4VM，对无标签数据不产生衰退现象，或安全半监督算法的MATLAB代码。-The package includes the MATLAB code of the semi-supervised algorithm S4VM, which towards making unlabeled data never hurt, or safe semi-supervised algorithm.
RSE-v1
- RSE (Regularized Selective Ensemble) is a selective ensemble learning algorithm for binay classification, which constructs ensemble under the regularization framework. In current version, the graph Laplacian serves as the regularizer, and unlabeled d
OLTV
- ＯＬＴＶ算法是使用一个标记的训练样本实现大量未标记样本的训练，是一个很好的分类器实现。-OLTV is a package for learning with only one labeled training example along with abundant unlabeled training instances, which is sufficient for building a good classifier.
SSDR
- SSDR是一种用于半监督降维的算法。它考虑到了未标记和成对约束来实现维数降低。-SSDR is a package for semi-supervised dimensionality reduction. This approach works for dimensionality reduction by considering unlabeled data and pairwise constraints.
vflib2
- 这个软件包是一个graph isomorphism算法库，主要包含VF2算法。-VFLib: graph matching library -------------------------------------------------------------------------------- The ARG Database is a huge collection of labeled and unlabeled graphs realized by the Intell
Fergus-Perona
- We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for al
232-GL
- 232无源隔离原理图，元器件未标号，需要的话可联系-232 passive isolation schematic, parts unlabeled, if required, contact
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
netlabel_unlabeled
- NetLabel Unlabeled Support driver for linux.
demoBagSVM
- 一种基于半监督的svm的图像分类方法。该方法通过聚类核的方法利用无标记样本局部正则化训练核的表达式。这种方法通过图像直接学习一个自适应的核。该程序仿真的是文章：Semi-supervised Remote Sensing Image Classification with Cluster Kernels。大家可以参考下。-A semi-supervised SVM is presented for the classification of remote sensing images. The
ECG-classification
- 心电图是一种经胸腔的以时间为单位记录心脏的电生理活动，并通过皮肤上的电极捕捉并记录下来的诊疗技术，能警示人体心脏健康状况。我们对十种不同心脏状况的心电图（ECG）数据进行实验，在对原始数据进行了一系列预处理后，利用PCA和ICA进行特征提取降维得到新的特征空间，并利用支持向量机进行训练，最后对未分类心电数据进行分类标记并评价其性能。-Electrocardiography (ECG) is a transthoracic interpretation of the electrical acti
Cost
- COST-SENSITIVE SEMI-SUPERVISED DISCRIMINANT ANALYSIS FOR FACE RECOGNITION Abstract: In our Project, we present a cost-sensitive semi-supervised discriminant analysis method for face recognition. In previous methods of dimensionality reductio
netlabel_unlabeled
- NetLabel Unlabeled Support for Linux v2.13.6.
CS4VM
- In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are unlabeled and different misclassification errors are associated with unequal costs. This scenario occurs in many real-world applications.
kMeans
- 机器学习算法，无监督学习，利用k均值聚类算法对未标注数据分组-Machine learning algorithms, unsupervised learning, the use of k-means clustering algorithm for unlabeled data packets
CCA
- 新型可信性聚类算法CCA，聚类性能比FCM和K-Means要好很多-A data clustering method partitions unlabeled data sets into clusters and labels them for various goals such as computer vision and pattern recognition.
neural-network
- 深度学习python实现，并附有MNIST上的测试程序，准确率98 以上-Deep learning learns low and high-level features large amounts of unlabeled data, improving classification on different, labeled, datasets. Deep learning can achieve an accuracy of 98 on the MNIST dataset.