Results 261 to 270 of about 24,916,518 (300)

Another Look at the Data Sparsity Problem

2006
Performance on a statistical language processing task relies upon accurate information being found in a corpus However, it is known (and this paper will confirm) that many perfectly valid word sequences do not appear in training corpora The percentage of n-grams in a test document which are seen in a training corpus is defined as n-gram coverage, and ...
Ben Allison, David Guthrie, Louise Guthrie   +2 more
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Addressing data sparsity in DNN acoustic modeling

2017 Twenty-third National Conference on Communications (NCC), 2017
This paper presents our work on developing acoustic models using deep neural networks (DNN) for low resource languages. This is considered one of the challenging problems in automatic speech recognition (ASR) as DNNs need large amount of data for building efficient models.
Seeram Tejaswi, Srinivasan Umesh
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Sparsity-Aware Data-Selective Adaptive Filters

IEEE Transactions on Signal Processing, 2014
We propose two adaptive filtering algorithms that combine sparsity-promoting schemes with data-selection mechanisms. Sparsity is promoted via some well-known nonconvex approximations to the l 0 norm in order to increase convergence speed of the algorithms when dealing with sparse/compressible signals. These approximations circumvent some difficulties
Markus V. S. Lima, Tadeu N. Ferreira, Wallace Alves Martins, Paulo S. R. Diniz   +3 more
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Data Sparsity Issues in the Collaborative Filtering Framework

2006
With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed.
Miha Grcar, Dunja Mladenic, Blaz Fortuna, Marko Grobelnik   +3 more
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Sparsity-based representation for categorical data

2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 2013
Over the past few decades, many algorithms have been continuously evolving in the area of machine learning. This is an era of big data which is generated by different applications related to various fields like medicine, the World Wide Web, E-learning networking etc.
Remya Menon, Shruthi S Nair, K Srindhya, M R Kaimal   +3 more
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Data Scheduling for Asynchronous Mini-batch Training on the Data of Sparsity

2016
A fast rate of convergence means that a given fixed amount of data can achieve a high level of prediction performance. In a fully asynchronous system, it is considered that the performance of the learning model could be reduced, because of the collision in parallel gradient computations.
Xiao Fang, Dongbo Dai, Huiran Zhang
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Dictionary Learning: From Data to Sparsity Via Clustering

Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics, 2015
Sparse representation based image and video processing have recently drawn much attention. Dictionary learning is an essential task in this framework. Our novel proposition involves direct computation of the dictionary by analyzing the distribution of training data in the metric space. The resulting representation is applied in the domain of grey scale
Rajesh Bhatt, Venkatesh K. Subramanian
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Sparsity Learning Formulations for Mining Time-Varying Data

IEEE Transactions on Knowledge and Data Engineering, 2015
Traditional clustering and feature selection methods consider the data matrix as static. However, the data matrices evolve smoothly over time in many applications. A simple approach to learn from these time-evolving data matrices is to analyze them separately. Such strategy ignores the time-dependent nature of the underlying data.
Rongjian Li, Wenlu Zhang, Yao Zhao 0001, Zhenfeng Zhu, Shuiwang Ji   +4 more
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Hyperspectral data compression using double sparsity model

2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015
The increased spatial and spectral resolution of hyperspectral images (HSIs) leads to very high data rates which causes difficulties in storing and transmitting all acquired data. Efficient compression techniques are desirable solution to this problem.
Qian Wu, Rong Zhang, Fan Wang
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Modifications of BIC for data mining under sparsity

2012
In many research areas today the number of features for which data is collected is much larger than the sample size based on which inference is made. This is especially true for applications in bioinformatics, but the theory presented here is of general interest in any data mining context, where the number of “interesting” features is expected to be ...
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