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...
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...
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 ...
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...
The algorithms use the labeled data as fodder for decision-making paradigms. This is in contrast to a different type of machine learning called unsupervised machine learning where unlabeled data is used. In unsupervised machine learning, the machine learning program has to evaluate data without ...
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
We assume that either view of the example would be su cient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment amuch smaller set of labeled examples. Speci cally, the presence of two distinct views of each...
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...
Another important issue with the text classification is the scarcity of labeled data. In this study, Combining Labeled and Unlabeled Data with Semantic Values of Terms (CLUDS) is presented. CLUDS has the following steps: preprocessing, instance labeling, combining labeled and unlabeled data, and ...
semi-supervised learningThis article addresses the problem of classification method based on both labeled and unlabeled data, where we assume that a density ... Kawano,Shuichi - 《Statistical Analysis & Data Mining》 被引量: 1发表: 2013年 Semi-supervised learning for predicting multivariate attribute...