Results 221 to 230 of about 38,536 (261)

Semi-supervised Classification of Chest Radiographs

2020
To train deep learning models in a supervised fashion, we need a significant amount of training data, but in most medical imaging scenarios, there is a lack of annotated data available. In this paper, we compare state-of-the-art semi-supervised classification methods in a medical imaging scenario.
Eduardo H. P. Pooch, Pedro L. Ballester, Rodrigo C. Barros   +2 more
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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 0001, Eamonn J. Keogh
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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 Mixture-of-Experts Classification

Fourth IEEE International Conference on Data Mining (ICDM'04), 2005
We introduce a mixture-of-experts technique that is a generalization of mixture modeling techniques previously suggested for semi-supervised learning. We apply the bias-variance decomposition to semi-supervised classification and use the decomposition to study the effects from adding unlabeled data when learning a mixture model.
Grigoris I. Karakoulas, Ruslan Salakhutdinov   +1 more
openaire   +1 more source

Semi-supervised classification with pairwise constraints

Neurocomputing, 2014
Graph-based semi-supervised learning has been intensively investigated for a long history. However, existing algorithms only utilize the similarity information between examples for graph construction, so their discriminative ability is rather limited.
Chen Gong 0002, Keren Fu, Qiang Wu 0001, Enmei Tu, Jie Yang 0002   +4 more
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Semi-supervised Ant Evolutionary Classification

2014
In this paper, we propose an ant evolutionary classification model, which treats different classes as ant colonies to classify the unlabeled instances. In our model, each ant colony sends its members to propagate its unique pheromone on the unlabeled instances. The unlabeled instances are treated as unlabeled ants.
Ping He 0001, Xiao-hua Xu 0001, Lin Lu 0002, Heng Qian, Wei Zhang, Kanwen Li   +5 more
openaire   +1 more source

Semi-supervised Classification and Noise Detection

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
Semi-supervised learning has become a topic of significant interests recently. In this paper, we are concerned with semi-supervised classification and noise detection. Based on label propagation algorithm, we present an improved label propagation algorithm, which can classify data and detect noise simultaneously.
Yunna Duan, Ying Gao, Xiaojuan Ren, Haoyuan Che, Keyang Yang   +4 more
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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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Semi-supervised classification with privileged information

International Journal of Machine Learning and Cybernetics, 2015
The privileged information that is available only for the training examples and not available for test examples, is a new concept proposed by Vapnik and Vashist (Neural Netw 22(5–6):544–557, 2009). With the help of the privileged information, learning using privileged information (LUPI) (Neural Netw 22(5–6):544–557, 2009) can significantly accelerate ...
Zhiquan Qi, Yingjie Tian 0001, Lingfeng Niu, Bo Wang 0049   +3 more
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Semi-supervised learning for image classification

2012
Objektklassifizierung ist ein aktives Forschungsgebiet in maschineller Bildverarbeitung was bisher nur unzureichend gelöst ist. Die meisten Ansätze versuchen die Aufgabe durch überwachtes Lernen zu lösen. Aber diese Algorithmen benötigen eine hohe Anzahl von Trainingsdaten um gut zu funktionieren. Das führt häufig entweder zu sehr kleinen Datensätzen (<
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