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Displaying 1-14 out of 14 total
Reducing Features to Improve Bug Prediction
Found in: Automated Software Engineering, International Conference on
By Shivkumar Shivaji, E. James Whitehead Jr., Ram Akella, Sunghun Kim
Issue Date:November 2009
pp. 600-604
Recently, machine learning classifiers have emerged as a way to predict the existence of a bug in a change made to a source code file. The classifier is first trained on software history data, and then used to predict bugs. Two drawbacks of existing classi...
 
A Fast Interactive Search System for Healthcare Services
Found in: 2012 Annual SRII Global Conference (SRII)
By Maria Daltayanni,Chunye Wang,Ram Akella
Issue Date:July 2012
pp. 525-534
In this paper we describe the design, development, and evaluation of a general human-machine interaction search system, and its potential and use in the context of a collaboration project with SAP and Saffron. The objective of a specialized version of the ...
 
Knowledge Extraction and Reuse within
Found in: Annual SRII Global Conference
By Chunye Wang, Ram Akella, Srikant Ramachandran, David Hinnant
Issue Date:April 2011
pp. 163-176
In this paper, we describe the initial version of a text analytics system under development and use at Cisco, where the objective is to
 
Semi-supervised Clustering Using Bayesian Regularization
Found in: Data Mining Workshops, International Conference on
By Zuobing Xu, Ram Akella, Mike Ching, Renjie Tang
Issue Date:October 2007
pp. 361-366
Text clustering is most commonly treated as a fully au- tomated task without user supervision. However, we can improve clustering performance using supervision in the form of pairwise (must-link and cannot-link) constraints. This paper introduces a rigorou...
 
Dynamic effects of ad impressions on commercial actions in display advertising
Found in: Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM '12)
By Aaron Flores, Jaimie Kwon, Joel Barajas, Marius Holtan, Ram Akella, Victor Andrei
Issue Date:October 2012
pp. 1747-1751
In this paper, we develop a time series approach, based on Dynamic Linear Models (DLM), to estimate the impact of ad impressions on the daily number of commercial actions when no user tracking is possible. The proposed method uses aggregate data, and hence...
     
The generalized dirichlet distribution in enhanced topic detection
Found in: Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM '12)
By Joel Barajas, Karla L. Caballero, Ram Akella
Issue Date:October 2012
pp. 773-782
We present a new, robust and computationally efficient Hierarchical Bayesian model for effective topic correlation modeling. We model the prior distribution of topics by a Generalized Dirichlet distribution (GD) rather than a Dirichlet distribution as in L...
     
Measuring dynamic effects of display advertising in the absence of user tracking information
Found in: Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy (ADKDD '12)
By Aaron Flores, Jaimie Kwon, Joel Barajas, Marius Holtan, Ram Akella, Victor Andrei
Issue Date:August 2012
pp. 1-9
In this paper, we develop a time series approach, based on Dynamic Linear Models (DLM), to estimate the impact of ad impressions on the daily number of commercial actions when no user tracking is possible. The proposed method uses aggregate data, and hence...
     
Marketing campaign evaluation in targeted display advertising
Found in: Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy (ADKDD '12)
By Aaron Flores, Jaimie Kwon, Joel Barajas, Marius Holtan, Ram Akella, Victor Andrei
Issue Date:August 2012
pp. 1-7
In this paper, we develop an experimental analysis to estimate the causal effect of online marketing campaigns as a whole, and not just the media ad design. We analyze the causal effects on user conversion probability. We run experiments based on A/B testi...
     
Incorporating statistical topic information in relevance feedback
Found in: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (SIGIR '12)
By Karla L. Caballero, Ram Akella
Issue Date:August 2012
pp. 1093-1094
Most of the relevance feedback algorithms only use document terms as feedback (local features) in order to update the query and re-rank the documents to show to the user. This approach is limited by the terms of those documents without any global context. ...
     
Measuring the effectiveness of display advertising: a time series approach
Found in: Proceedings of the 20th international conference companion on World wide web (WWW '11)
By Brad Null, Jaimie Kwon, Joel Barajas, Marius Holtan, Ram Akella
Issue Date:March 2011
pp. 7-8
We develop an approach for measuring the effectiveness of online display advertising at the campaign level. We present a Kalman filtering approach to deseasonalize and estimate the percentage changes of online sales on a daily basis. For this study, we ana...
     
Hierarchical service analytics for improving productivity in an enterprise service center
Found in: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM '10)
By Chunye Wang, Ram Akella, Srikant Ramachandran
Issue Date:October 2010
pp. 1209-1218
Modern day service centers are the building blocks for highly efficient and productive business systems in a knowledge economy. In these service systems, accurate and timely delivery of pertinent information to service representatives becomes the cornersto...
     
Active relevance feedback for difficult queries
Found in: Proceeding of the 17th ACM conference on Information and knowledge mining (CIKM '08)
By Ram Akella, Zuobing Xu
Issue Date:October 2008
pp. 1001-1001
Relevance feedback has been demonstrated to be an effective strategy for improving retrieval accuracy. The existing relevance feedback algorithms based on language models and vector space models are not effective in learning from negative feedback document...
     
A new probabilistic retrieval model based on the dirichlet compound multinomial distribution
Found in: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '08)
By Ram Akella, Zuobing Xu
Issue Date:July 2008
pp. 2-2
The classical probabilistic models attempt to capture the Ad hoc information retrieval problem within a rigorous probabilistic framework. It has long been recognized that the primary obstacle to effective performance of the probabilistic models is the need...
     
A bayesian logistic regression model for active relevance feedback
Found in: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '08)
By Ram Akella, Zuobing Xu
Issue Date:July 2008
pp. 2-2
Relevance feedback, which traditionally uses the terms in the relevant documents to enrich the user's initial query, is an effective method for improving retrieval performance. The traditional relevance feedback algorithms lead to overfitting because of th...
     
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