14 results match your criteria Knowledge and Information Systems [Journal]

  • Page 1 of 1

Crowd labeling latent Dirichlet allocation.

Knowl Inf Syst 2017 Dec 19;53(3):749-765. Epub 2017 Apr 19.

Department of Electrical and Computer Engineering, University of California at San Diego, San Diego, CA 92122, USA.

Large, unlabeled datasets are abundant nowadays, but getting labels for those datasets can be expensive and time-consuming. Crowd labeling is a crowdsourcing approach for gathering such labels from workers whose suggestions are not always accurate. While a variety of algorithms exist for this purpose, we present crowd labeling latent Dirichlet allocation (CL-LDA), a generalization of latent Dirichlet allocation that can solve a more general set of crowd labeling problems. Read More

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http://dx.doi.org/10.1007/s10115-017-1053-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223327PMC
December 2017

Binary Classifier Calibration Using an Ensemble of Piecewise Linear Regression Models.

Knowl Inf Syst 2018 Jan 17;54(1):151-170. Epub 2017 Nov 17.

Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA.

In this paper we present a new non-parametric calibration method called (ENIR). The method can be considered as an extension of BBQ (Pakdaman Naeini, Cooper and Hauskrecht, 2015b), a recently proposed calibration method, as well as the commonly used calibration method based on isotonic regression (IsoRegC) (Zadrozny and Elkan, 2002). ENIR is designed to address the key limitation of IsoRegC which is the monotonicity assumption of the predictions. Read More

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http://dx.doi.org/10.1007/s10115-017-1133-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875942PMC
January 2018
3 Reads

Markov Logic Networks for Adverse Drug Event Extraction from Text.

Knowl Inf Syst 2017 May 8;51(2):435-457. Epub 2016 Aug 8.

Indiana University, Marshfield Clinic.

Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g. Read More

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http://dx.doi.org/10.1007/s10115-016-0980-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5673137PMC
May 2017
8 Reads

Collegial Activity Learning between Heterogeneous Sensors.

Knowl Inf Syst 2017 Nov 27;53(2):337-364. Epub 2017 Mar 27.

School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164.

Activity recognition algorithms have matured and become more ubiquitous in recent years. However, these algorithms are typically customized for a particular sensor platform. In this paper we introduce PECO, a Personalized activity ECOsystem, that transfers learned activity information seamlessly between sensor platforms in real time so that any available sensor can continue to track activities without requiring its own extensive labeled training data. Read More

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http://dx.doi.org/10.1007/s10115-017-1043-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5627625PMC
November 2017
6 Reads

A Survey of Methods for Time Series Change Point Detection.

Knowl Inf Syst 2017 May 8;51(2):339-367. Epub 2016 Sep 8.

School of Electrical Engineering and Computer Science Washington State University, Pullman, WA.

Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. Read More

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http://dx.doi.org/10.1007/s10115-016-0987-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5464762PMC
May 2017
7 Reads

Diffusion archeology for diffusion progression history reconstruction.

Knowl Inf Syst 2016 Nov 11;49(2):403-427. Epub 2015 Dec 11.

Computational Biology Department, Carnegie Mellon University, Pittsburgh PA, USA.

Diffusion through graphs can be used to model many real-world processes, such as the spread of diseases, social network memes, computer viruses, or water contaminants. Often, a real-world diffusion cannot be directly observed while it is occurring - perhaps it is not noticed until some time has passed, continuous monitoring is too costly, or privacy concerns limit data access. This leads to the need to reconstruct how the present state of the diffusion came to be from partial diffusion data. Read More

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http://dx.doi.org/10.1007/s10115-015-0904-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5095933PMC
November 2016
6 Reads

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network.

Knowl Inf Syst 2016 Nov 31;49(2):455-479. Epub 2015 Dec 31.

Computer and Information Science Department, University of Oregon.

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. Read More

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5058368PMC
http://dx.doi.org/10.1007/s10115-015-0910-zDOI Listing
November 2016
12 Reads

Transfer Learning for Class Imbalance Problems with Inadequate Data.

Knowl Inf Syst 2016 Jul 25;48(1):201-228. Epub 2015 Aug 25.

Department of Computer Science, Wayne State University, Detroit, MI.

A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. Read More

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http://dx.doi.org/10.1007/s10115-015-0870-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4929860PMC
July 2016
2 Reads

An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data.

Knowl Inf Syst 2016 Jan 21;46(1):115-150. Epub 2015 Jan 21.

Department of Computer Science, University of Pittsburgh,

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. Read More

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http://dx.doi.org/10.1007/s10115-015-0819-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4704806PMC
January 2016
7 Reads

Indexing Volumetric Shapes with Matching and Packing.

Knowl Inf Syst 2015 Apr;43(1):157-180

Department of Computational and Systems Biology, University of Pittsburgh.

We describe a novel algorithm for bulk-loading an index with high-dimensional data and apply it to the problem of volumetric shape matching. Our algorithm is a general approach for packing data according to a similarity metric. First an approximate -nearest neighbor graph is constructed using , an improvement to previous work that decreases construction time while improving the quality of approximation. Read More

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http://dx.doi.org/10.1007/s10115-014-0729-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4465823PMC
April 2015
5 Reads

Efficient Mining of Discriminative Co-clusters from Gene Expression Data.

Knowl Inf Syst 2014 Dec;41(3):667-696

Department of Computer Science, Wayne State University, Detroit, MI, 48202.

Discriminative models are used to analyze the differences between two classes and to identify class-specific patterns. Most of the existing discriminative models depend on using the entire feature space to compute the discriminative patterns for each class. Co-clustering has been proposed to capture the patterns that are correlated in a subset of features, but it cannot handle discriminative patterns in labeled datasets. Read More

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http://dx.doi.org/10.1007/s10115-013-0684-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4308820PMC
December 2014
4 Reads

Hyper-structure mining of frequent patterns in uncertain data streams.

Knowl Inf Syst 2013 Oct;37(1):219-244

Department of Computer Science and Engineering, University of South Florida, 4202 E. Fowler Ave., ENB 118, Tampa, FL 33620, USA.

Data uncertainty is inherent in many real-world applications such as sensor monitoring systems, location-based services, and medical diagnostic systems. Moreover, many real-world applications are now capable of producing continuous, unbounded data streams. During the recent years, new methods have been developed to find frequent patterns in uncertain databases; nevertheless, very limited work has been done in discovering frequent patterns in uncertain data streams. Read More

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http://dx.doi.org/10.1007/s10115-012-0581-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3983695PMC
October 2013
5 Reads

Transfer Learning for Activity Recognition: A Survey.

Knowl Inf Syst 2013 Sep;36(3):537-556

Department of Electrical Engineering and Computer Science, Washington State University, Pullman WA, USA.

Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. Read More

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http://dx.doi.org/10.1007/s10115-013-0665-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3768027PMC
September 2013
10 Reads

Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data.

Knowl Inf Syst 2006 Feb;9(2):157-179

Department of Computer Science, Artificial Intelligence Research Laboratory, Computational Intelligence, Learning, and Discovery Program, Iowa State University, Ames, Iowa 50011-1040, USA

In many application domains, there is a need for learning algorithms that can effectively exploit attribute value taxonomies (AVT)-hierarchical groupings of attribute values-to learn compact, comprehensible and accurate classifiers from data-including data that are partially specified. This paper describes AVT-NBL, a natural generalization of the naïve Bayes learner (NBL), for learning classifiers from AVT and data. Our experimental results show that AVT-NBL is able to generate classifiers that are substantially more compact and more accurate than those produced by NBL on a broad range of data sets with different percentages of partially specified values. Read More

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http://link.springer.com/10.1007/s10115-005-0211-z
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http://dx.doi.org/10.1007/s10115-005-0211-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2846370PMC
February 2006
3 Reads
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