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An unsupervised attribute clustering algorithm for unsupervised feature selection
2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015The curse of dimensionality refers to the problem that one faces when analyzing datasets with thousands or hundreds of thousands of attributes. This problem is usually tackled by different feature selection methods which have been shown to effectively reduce computation time, improve prediction performance, and facilitate better understanding of ...
Pei-Yuan Zhou, Keith C. C. Chan
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Neural Computation, 1989
What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments? This question is reviewed in the light of connectionist models of unsupervised learning and some older ideas, namely the cognitive maps and working models of Tolman and Craik, and the idea that redundancy is important for ...
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What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments? This question is reviewed in the light of connectionist models of unsupervised learning and some older ideas, namely the cognitive maps and working models of Tolman and Craik, and the idea that redundancy is important for ...
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Unsupervised Video Surveillance
2011This paper addresses the problem of automatically learning common behaviors from long time observations of a scene of interest, with the purpose of classifying actions and, possibly, detecting anomalies. Unsupervised learning is used as an effective way to extract information from the scene with a very limited intervention of the user.
NOCETI, NICOLETTA, ODONE, FRANCESCA
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Adherence to Unsupervised Exercise
The Physician and Sportsmedicine, 1983In brief: Forty-seven male police officers (mean age 41.0 years) were randomly assigned to three exercise groups: unsupervised, supervised, and control. Training consisted of walking and jogging three days per week for 20 weeks. The attrition rate for the unsupervised group (35%) was lower than for the supervised group (45%).
L R, Gettman, M L, Pollock, A, Ward
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Unsupervised pronunciation validation
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009This paper addresses selecting between candidate pronunciations for out-of-vocabulary words in speech processing tasks. We introduce a simple, unsupervised method that outperforms the conventional supervised method of forced alignment with a reference. The success of this method is independently demonstrated using three metrics from large-scale speech ...
Christopher M. White +6 more
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Unsupervised Recognition of ADLs
2010In this paper we present an approach to the unsupervised recognition of activities of daily living (ADLs) in the context of smart environments The developed system utilizes background domain knowledge about the user activities and the environment in combination with probabilistic reasoning methods in order to build best possible explanation of the ...
Todor Dimitrov +2 more
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Unsupervised Learning: Clustering
2019In this article an introduction on unsupervised cluster analysis is provided. Clustering is the organisation of unlabelled data into similarity groups called clusters. A cluster is a collection of data items which are similar between them, and dissimilar to data items in other clusters.
Serra A., Tagliaferri R.
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Spicules for Unsupervised Learning
2009We present a new model of unsupervised competitive neural network, based on spicules. This model is capable of detecting topological information of an input space, determining its orientation and, in most case, its skeleton.
José Antonio Gómez-Ruiz +2 more
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Unsupervised possibilistic clustering
Pattern Recognition, 2006In fuzzy clustering, the fuzzy c-means (FCM) clustering algorithm is the best known and used method. Since the FCM memberships do not always explain the degrees of belonging for the data well, Krishnapuram and Keller proposed a possibilistic approach to clustering to correct this weakness of FCM.
Miin-Shen Yang, Kuo-Lung Wu
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Multilingual Unsupervised Dependency Parsing with Unsupervised POS Tags
2015In this paper, we present experiments with unsupervised dependency parser without using any part-of-speech tags learned from manually annotated data. We use only unsupervised word-classes and therefore propose fully unsupervised approach of sentence structure induction from a raw text.
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