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Estimation of probabilities from sparse data for the language model component of a speech recognizer

by Slava M. Katz - IEEE Transactions on Acoustics, Speech and Signal Processing , 1987
"... Abstract-The description of a novel type of rn-gram language model is given. The model offers, via a nonlinear recursive procedure, a com-putation and space efficient solution to the problem of estimating prob-abilities from sparse data. This solution compares favorably to other proposed methods. Wh ..."
Abstract - Cited by 799 (2 self) - Add to MetaCart
Abstract-The description of a novel type of rn-gram language model is given. The model offers, via a nonlinear recursive procedure, a com-putation and space efficient solution to the problem of estimating prob-abilities from sparse data. This solution compares favorably to other proposed methods

SPARSE DATA

by Gil Shabat, Gil Shabat, Gil Shabat , 2008
"... Yaroslavsky. His wide knowledge, patience and personal guidance throughout this work have been a great value for me. I feel very fortunate for having a supervisor like you. I wish to express my sincere gratitude to Barak Fishbain, for constructive and interesting discussions and for valuable advices ..."
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Yaroslavsky. His wide knowledge, patience and personal guidance throughout this work have been a great value for me. I feel very fortunate for having a supervisor like you. I wish to express my sincere gratitude to Barak Fishbain, for constructive and interesting discussions and for valuable advices and friendly help. i A common distortion in videos is image instability in the form of chaotic global and local displacements of image frames caused by camera instability, fluctuations in the refraction index of the light propagation media and similar factors. Such videos that very frequently present moving objects on a stable background contain tremendous redundancy that potentially can be used for image stabilization and perfecting provided reliable separation of stable background from true moving objects. Recently, it was proposed to use this redundancy for resolution enhancement of video through elastic registration, with sub-pixel accuracy, of segments of video frames which represent stable scenes.

Predicting with Sparse Data

by Martin Shepperd, Michelle Cartwright , 2000
"... It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable an ..."
Abstract - Cited by 32 (0 self) - Add to MetaCart
and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process. Our minimum

Robust Estimation for Sparse Data

by Wen-hui Lo, Sin-horng Chen
"... Robust parameters estimation of sparse data is generally applied to the test cases of time-consuming or high cost data collection. This study concerns with the problem in small sample size which is often encountered in the client data processing for speaker verification. We found that there always e ..."
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Robust parameters estimation of sparse data is generally applied to the test cases of time-consuming or high cost data collection. This study concerns with the problem in small sample size which is often encountered in the client data processing for speaker verification. We found that there always

Predicting with Sparse Data 1

by Michelle Cartwright, Talbot Campus
"... It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and sys ..."
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and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our

Non-negative matrix factorization with sparseness constraints,”

by Patrik O Hoyer , Patrik Hoyer@helsinki , Fi - Journal of Machine Learning Research, , 2004
"... Abstract Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we sho ..."
Abstract - Cited by 498 (0 self) - Add to MetaCart
show how explicitly incorporating the notion of 'sparseness' improves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF and for our extension. Our hope is that this will further the application of these methods to solving novel data

Behavior recognition via sparse spatio-temporal features

by Piotr Dollár, Vincent Rabaud, Garrison Cottrell, Serge Belongie - In VS-PETS , 2005
"... A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas to the spatio ..."
Abstract - Cited by 717 (4 self) - Add to MetaCart
A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas

Data mules: Modeling a three-tier architecture for sparse sensor networks

by Rahul C. Shah, Sumit Roy, Sushant Jain, Waylon Brunette - IN IEEE SNPA WORKSHOP , 2003
"... Abstract — This paper presents and analyzes an architecture that exploits the serendipitous movement of mobile agents in an environment to collect sensor data in sparse sensor networks. The mobile entities, called MULEs, pick up data from sensors when in close range, buffer it, and drop off the data ..."
Abstract - Cited by 485 (6 self) - Add to MetaCart
Abstract — This paper presents and analyzes an architecture that exploits the serendipitous movement of mobile agents in an environment to collect sensor data in sparse sensor networks. The mobile entities, called MULEs, pick up data from sensors when in close range, buffer it, and drop off

Annotations for sparse data streams

by Amit Chakrabarti , Graham Cormode , Navin Goyal , Justin Thaler - In SODA , 2014
"... Abstract Motivated by the surging popularity of commercial cloud computing services, a number of recent works have studied annotated data streams and variants thereof. In this setting, a computationally weak verifier (cloud user), lacking the resources to store and manipulate his massive input loca ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
that have no non-trivial standard data stream algorithms. However, even though optimal schemes are now known for several basic problems, such optimality holds only for streams whose length is commensurate with the size of the data universe. In contrast, many real-world data sets are relatively sparse

Integrating dense and sparse data partitioning

by Javier Fresno, Diego R. Llanos , 2011
"... Layout methods for dense and sparse data are often seen as two separate prob-lems with its own particular techniques. However, they are based on the same basic concepts. This paper studies how to integrate automatic data-layout and partition techniques for both dense and sparse data structures. In p ..."
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Layout methods for dense and sparse data are often seen as two separate prob-lems with its own particular techniques. However, they are based on the same basic concepts. This paper studies how to integrate automatic data-layout and partition techniques for both dense and sparse data structures
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