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 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...
Whileunlabeled dataconsists of raw inputs with no designated outcome, labeled data is precisely the opposite. Labeled data is carefully annotated with meaningful tags, or labels, that classify the data's elements or outcomes. For example, in a dataset of emails, each email might be labeled as ...
In the proposed loss function, we combine the classifier predictions, based on the labeled data, and the pairwise similarity between labeled and unlabeled examples. The main goal of the proposed loss function is to minimize the inconsistency between classifier predictions and the pairwise similarity....
Combining labeled and unlabeled data with co-training:(与co-training结合标记和未标记数据).pdf,Combining Lab eled and Unlab eled Data with CoTraining y Avrim Blum Tom Mitchell School of Computer Science School of Computer Science Carnegie Mellon Univer
Learning Classification with Both Labeled and Unlabeled Data A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of hand-labeled examples. Labeling large amount of data is a costly process which in many cases is prohibitive. In ....
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...
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
Blumand Mitchell (1998) proposes a co-training algorithm to make use of unlabeled data to boost the performance of a learning algorithm. They assume that the data can be described by two separate feature sets which are not com- pletely correlated, and each of which is predictive enough ...
Then, the estimated class membership probabilities are used to label and weight unlabeled instances. At last, a naive Bayes is trained again using both the originally labeled data and the (newly labeled and weighted) unlabeled data. Our experimental results based on a large number of UCI data ...