the unlabeled data provides information about the structure of the domain. 主要算法及思想介绍: 1. Self-Training 分类器在labeled data上进行训练,然后用其对unlabeled data进行分类。the most confident unlabeled points(对无标签数据分类后的信任度),伴随着它们预测的标签,加入到训练集中。这个过程重复进行直到收...
This article addresses the problem of classification method based on both labeled and unlabeled data, where we assume that a density function for labeled data is different from that for unlabeled data. We propose a semi-supervised logistic regression model for classification problem along with the ...
Labeled data is raw data that has been assigned labels to add context or meaning, which is used to train machine learning models in supervised learning.
In this paper we address the problem of text classification with labeled data and unlabeled data. We propose a Latent Bayes Ensemble model based on word-concept mapping and transductive boosting method. With the knowledge extracted from ontologies, we hope to improve the classification accuracy even...
Zhu X. and Ghahramani Z. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, 2002. 概 本文通过将有标签数据传播给无标签数据
Some companies, such as Drive, are using deep learning to enhance automation for annotating data, as a way to accelerate the tedious process of data labelling. Let’s use unlabeled data Koopman, however, believes there is another way to “squeeze the value out of the accumulated data.” How...
1) labeled data 标记数据1. For this method,in the training process,a simple classifier based on the idea of each class having a center is designed,which can select efficient data from plenty of unlabeled data to label and then add into the SVM training set. 该方法在训练过程中,先构造一...
Combining_labeled_and_unlabeled_data_with_co-training 下载积分: 3000 内容提示: Combining Lab eled and Unlab eled Data with Co-Training? yAvrim BlumScho ol of Computer ScienceCarnegie Mellon UniversityPittsburgh, PA 15213-3891avrim+@cs.cmu.eduTom MitchellScho ol of Computer ScienceCarnegie Mellon...
Thus, semi-supervised learning, which attempts to benefit from large amount of unlabeled data together with labeled data, has attracted much attention from researchers. In this paper, we propose a very fast and yet highly effective semi-supervised learning algorithm. We call our proposed algorithm ...
Due to the considerable time and expense required in labeling data, a challenge is to propose learning algorithms that can learn from a small amount of labeled data and a much larger amount of unlabeled data. In this paper, we propose one such algorithm which uses an evolutionary strategy to...