Results 261 to 270 of about 482,653 (299)

Transferred Feature Selection

2009 IEEE International Conference on Data Mining Workshops, 2009
Traditional feature selection algorithms require a large number of labeled training instances to find out the most informative subset of features. However, in many real-world applications, the labeled data are often difficult, expensive or time-consuming to obtain.
Wei Bi, Yuan Shi, Zhen-zhong Lan
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Hierarchical Energy-transfer Features

Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, 2014
In the paper, we propose the novel and efficient object descriptors that are designed to describe the appearance of the objects. The descriptors are called as Hierarchical Energy-Transfer Features (HETF). The main idea behind HETF is that the shape of the objects can be described by the function of energy distribution.
Radovan Fusek, Eduard Sojka, Karel Mozdren, Milan Surkala   +3 more
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Feature subspace transfer for collaborative filtering

Neurocomputing, 2014
Abstract The sparsity problem is a major bottleneck for the collaborative filtering. Recently, transfer learning methods are introduced in collaborative filtering to alleviate the sparsity problem which aim to use the shared knowledge in related domains to help improve the prediction performance.
Jing Wang 0049, Liangwen Ke
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Anomaly Subgraph Detection with Feature Transfer

Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020
Anomaly detection in multilayer graphs becomes more critical in many application scenarios, i.e., identifying crime hotspots in urban areas by discovering suspicious and illicit behaviors in social networks. However, it is a big challenge to identify anomalies in a layer graph due to the insufficient anomaly features.
Ying Sun 0005, Wenjun Wang 0002, Nannan Wu, Wei Yu 0016, Xue Chen 0005   +4 more
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Energy-Transfer Features for Pedestrian Detection

2013
In this paper, we propose an interesting and novel method for computing the image features that are useful for object detection. The method is interesting and novel in the terms of the feature vector dimensionality and object information capturing.
Radovan Fusek, Eduard Sojka, Karel Mozdren, Milan Surkala   +3 more
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Features of writtenness transferred

2011
This paper emphasizes language contact situations in general as an origin of hybrid features of writtenness. Furthermore it stresses the necessity of taking into account the medial-conceptional differences between languages of distance (prototypically written) and languages of proximity (prototypically spoken) when analyzing language contact data and ...
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Transfer Effects in Feature-Positive and Feature-Negative Learning by Adult Humans

The American Journal of Psychology, 1981
In two experiments, college students performed a feature-positive or a feature-negative discrimination task based on colors or symbols and were then transferred to a feature-positive or feature-negative discrimination based on the other stimulus dimension (symbols-colors, colors-symbols).
G B, Nallan, M B, Brown, C, Edmonds, V, Gillham, K, Kowalewski, J S, Miller   +5 more
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Acoustic feature conversion using a polynomial based feature transferring algorithm

The 9th International Symposium on Chinese Spoken Language Processing, 2014
This study proposes a polynomial based feature transferring (PFT) algorithm for acoustic feature conversion. The PFT process consists of estimation and conversion phases. The estimation phase aims to compute a polynomial based transfer function using only a small set of parallel source and target features.
Syu-Siang Wang, Payton Lin, Dau-Cheng Lyu, Yu Tsao 0001, Hsin-Te Hwang, Borching Su   +5 more
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A feature of heat transfer to organic heat-transfer media

Journal of Engineering Physics, 1986
It is shown that the nature of the changes in the wall temperature during heat transfer to an organic heat-transfer medium accompanied by the formation of deposits depends strongly on the roughness of the surface.
N. L. Kafengauz, V. A. Gladkikh
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Transfer Learning for Tandem ASR Feature Extraction

2008
Tandem automatic speech recognition (ASR), in which one or an ensemble of multi-layer perceptrons (MLPs) is used to provide a non-linear transform of the acoustic parameters, has become a standard technique in a number of state-of-the-art systems. In this paper, we examine the question of how to transfer learning from out-of-domain data to new tasks.
Joe Frankel, Özgür Çetin, Nelson Morgan   +2 more
openaire   +1 more source