Location:
Search - unlabeled
Search list
Description: 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.
Platform: |
Size: 6144 |
Author: 李平 |
Hits:
Description: .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 additional unlabeled data that does not share the labels of the supervised problem and does not arise from the same distribution.
Platform: |
Size: 2048 |
Author: huneza |
Hits:
Description: 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.
Platform: |
Size: 14336 |
Author: lazi |
Hits:
Description: 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.
Platform: |
Size: 280576 |
Author: 王云 |
Hits:
Description: 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 data can also be exploited to improve the performance.
Platform: |
Size: 23552 |
Author: 123 |
Hits:
Description: 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.
Platform: |
Size: 18432 |
Author: |
Hits:
Description: 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.
Platform: |
Size: 2048 |
Author: |
Hits:
Description: 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 Intelligent Systems and Artificial Vision Lab. (SIVALab) of the University of Naples ``Federico II . The aim of this collection is to provide the graph research community with a standard test ground for the benchmarking of graph matching algorithms.
The database is organized in two section: labeled and unlabeled graphs. Both labeled and unlabeled graphs have been randomly generated according to six different generation models, each involving different possible parameter settings. As a result, 168 diverse kinds of graphs are contained in the database. Each type of unlabeled graph is represented by thousands of pairs of graphs for which an isomorphism or a graph-subgraph isomorphism relation holds, for a total of 143,600 graphs. Furthermore, each type of labeled graph is represented by th
Platform: |
Size: 206848 |
Author: fangjun |
Hits:
Description: 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 all aspects of the object: shape, appearance, occlusion
and relative scale. An entropy-based feature detector
is used to select regions and their scale within the image. In
learning the parameters of the scale-invariant object model
are estimated. This is done using expectation-maximization
in a maximum-likelihood setting. In recognition, this model
is used in a Bayesian manner to classify images. The flexible
nature of the model is demonstrated by excellent results
over a range of datasets including geometrically constrained
classes (e.g. faces, cars) and flexible objects (such
as animals).
Platform: |
Size: 3409920 |
Author: Daria |
Hits:
Description: 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
Platform: |
Size: 4096 |
Author: 大人 |
Hits:
Description: 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 rigorous way the labels from the labeled data to the unlabeled.
Applications in various areas in the Machine learning, such as text processing, image processing, pattern recognition, and others.
Platform: |
Size: 2542592 |
Author: stefan.petrov.beb |
Hits:
Description: NetLabel Unlabeled Support driver for linux.
Platform: |
Size: 8192 |
Author: kingconjie |
Hits:
Description: 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 method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image, and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictionsds
Platform: |
Size: 108544 |
Author: 姜红茹 |
Hits:
Description: 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 activity of the heart over a period of time, which can reflect the physical condition of a heart. We carry a series of experiments based on ten sets of different ECG data. First, we pre-process the raw data with data processing technology, and then we use the feature extraction technology of PCA and ICA to get a new feature space. At last we use support vector machine to train a model which can and finally predict and classify the unlabeled ECG data. We validate and optimize the model by evaluating its performance when training it.
Platform: |
Size: 5120 |
Author: Kevin Lee |
Hits:
Description: 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 reduction, they aim to seek low-dimensional feature representations to achieve low classification errors.
In our algorithm, we proposed a new method to learn a discriminative feature subspace by making use of both labeled and unlabeled samples and exploring different cost information of all the training samples simultaneously.
Platform: |
Size: 62464 |
Author: robin |
Hits:
Description: NetLabel Unlabeled Support for Linux v2.13.6.
Platform: |
Size: 9216 |
Author: duxonxi |
Hits:
Description: 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. For example, in some disease
diagnosis, the cost of erroneously diagnosing a patient
as healthy is much higher than that of diagnosing a
healthy person as a patient. Also, the acquisition of labeled
data requires medical diagnosis which is expensive,
while the collection of unlabeled data such as basic
health information is much cheaper
Platform: |
Size: 452608 |
Author: lin |
Hits:
Description: 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
Platform: |
Size: 2048 |
Author: 王铭航 |
Hits:
Description: 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.
Platform: |
Size: 1480704 |
Author: 周建栋 |
Hits:
Description: 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.
Platform: |
Size: 2017280 |
Author: 孙立立 |
Hits: