Results 221 to 230 of about 259,134 (247)

Convex Multiview Semi-Supervised Classification

IEEE Transactions on Image Processing, 2017
In many practical applications, there are a great number of unlabeled samples available, while labeling them is a costly and tedious process. Therefore, how to utilize unlabeled samples to assist digging out potential information about the problem is very important. In this paper, we study a multiclass semi-supervised classification task in the context
Feiping Nie, Jing Li, Xuelong Li
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Safety-Aware Semi-Supervised Classification

IEEE Transactions on Neural Networks and Learning Systems, 2013
Though semi-supervised classification learning has attracted great attention over past decades, semi-supervised classification methods may show worse performance than their supervised counterparts in some cases, consequently reducing their confidence in real applications.
Yunyun, Wang, Songcan, Chen
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Semi-supervised classification trees

Journal of Intelligent Information Systems, 2017
In many real-life problems, obtaining labelled data can be a very expensive and laborious task, while unlabeled data can be abundant. The availability of labeled data can seriously limit the performance of supervised learning methods. Here, we propose a semi-supervised classification tree induction algorithm that can exploit both the labelled and ...
Jurica Levatić, Michelangelo Ceci, Dragi Kocev, Sašo Džeroski   +3 more
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Differentially Private Semi-Supervised Classification

2017 IEEE International Conference on Smart Computing (SMARTCOMP), 2017
In this work, we propose a novel framework for linear classification, differentially private semi-supervised classification. The previous method in the classification problem, differentially private empirical risk minimization (ERM) only generates a classifier from labeled data.
Xu Long, Jun Sakuma
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Semi-supervised time series classification

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006
The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data. In reality, such data may be very difficult or expensive to obtain. For example, it may require the time and expertise of cardiologists, space launch technicians, or other ...
Li Wei, Eamonn Keogh
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SEMI-SUPERVISED CLASSIFICATION USING BRIDGING

International Journal on Artificial Intelligence Tools, 2008
Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging ...
CHAN, Jason Yuk Hin, KOPRINSKA, Irena, POON, Josiah   +2 more
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Semi-supervised Classification Forests

2013
Previous chapters have discussed the use of decision forests in supervised problems as well as unsupervised ones. This chapter puts the two things together to achieve semi-supervised learning. We focus here on semi-supervised classification, but the approach can be extended to regression too.
A. Criminisi, J. Shotton
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Semi-Supervised Music Genre Classification

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
Music genre classification is a hot topic in pattern recognition and signal processing. Classical supervised methods need lost of labeled music data to train a classifier. In this paper, we propose a semi-supervised genre classification algorithm which is developed on several labeled music tracks and lots of unlabelled tracks.
Yangqiu Song, Changshui Zhang, Shiming Xiang   +2 more
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Ant Based Semi-supervised Classification

2010
Semi-supervised classification methods make use of the large amounts of relatively inexpensive available unlabeled data along with the small amount of labeled data to improve the accuracy of the classification. This article presents a novel 'self-training' based semi-supervised classification algorithm using the property of aggregation pheromone found ...
Anindya Halder, Susmita Ghosh, Ashish Ghosh   +2 more
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