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Machine Learning

2013

Articles 1 - 5 of 5

Full-Text Articles in Artificial Intelligence and Robotics

Will We Connect Again? Machine Learning For Link Prediction In Mobile Social Networks, Ole J. Mengshoel, Raj Desai, Andrew Chen, Brian Tran Jul 2013

Will We Connect Again? Machine Learning For Link Prediction In Mobile Social Networks, Ole J. Mengshoel, Raj Desai, Andrew Chen, Brian Tran

Ole J Mengshoel

In this paper we examine link prediction for two types of data sets with mobility data, namely call data records (from the MIT Reality Mining project) and location-based social networking data (from the companies Gowalla and Brightkite). These data sets contain location information, which we incorporate in the features used for prediction. We also examine different strategies for data cleaning, in particular thresholding based on the amount of social interaction. We investigate the machine learning algorithms Decision Tree, Naïve Bayes, Support Vector Machine, and Logistic Regression. Generally, we find that our feature selection and filtering of the data sets have …

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Optimizing Parallel Belief Propagation In Junction Trees Using Regression, Lu Zheng, Ole J. Mengshoel Jul 2013

Optimizing Parallel Belief Propagation In Junction Trees Using Regression, Lu Zheng, Ole J. Mengshoel

Ole J Mengshoel

The junction tree approach, with applications in artificial intelligence, computer vision, machine learning, and statistics, is often used for computing posterior distributions in probabilistic graphical models. One of the key challenges associated with junction trees is computational, and several parallel computing technologies - including many-core processors - have been investigated to meet this challenge. Many-core processors (including GPUs) are now programmable, unfortunately their complexities make it hard to manually tune their parameters in order to optimize software performance. In this paper, we investigate a machine learning approach to minimize the execution time of parallel junction tree algorithms implemented on a …

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Latent Topic Analysis For Predicting Group Purchasing Behavior On The Social Web, Feng-Tso Sun, Martin Griss, Ole J. Mengshoel, Yi-Ting Yeh Jun 2013

Latent Topic Analysis For Predicting Group Purchasing Behavior On The Social Web, Feng-Tso Sun, Martin Griss, Ole J. Mengshoel, Yi-Ting Yeh

Ole J Mengshoel

Group-deal websites, where customers purchase products or services in groups, are an interesting phenomenon on the Web. Each purchase is kicked o#11;ff by a group initiator, and other customers can join in. Customers form communities with people with similar interests and preferences (as in a social network), and this drives bulk purchasing (similar to online stores, but in larger quantities per order, thus customers get a better deal). In this work, we aim to better understand what factors in influence customers' purchasing behavior for such social group-deal websites. We propose two probabilistic graphical models, i.e., a product-centric inference model (PCIM) …

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Mobile Computing: Challenges And Opportunities For Autonomy And Feedback, Ole J. Mengshoel, Bob Iannucci, Abe Ishihara May 2013

Mobile Computing: Challenges And Opportunities For Autonomy And Feedback, Ole J. Mengshoel, Bob Iannucci, Abe Ishihara

Ole J Mengshoel

Mobile devices have evolved to become computing platforms more similar to desktops and workstations than the cell phones and handsets of yesteryear. Unfortunately, today’s mobile infrastructures are mirrors of the wired past. Devices, apps, and networks impact one another, but a systematic approach for allowing them to cooperate is currently missing. We propose an approach that seeks to open key interfaces and to apply feedback and autonomic computing to improve both user experience and mobile system dynamics.

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Spoons: Netflix Outage Detection Using Microtext Classification, Eriq A. Augusitne Mar 2013

Spoons: Netflix Outage Detection Using Microtext Classification, Eriq A. Augusitne

Master's Theses

Every week there are over a billion new posts to Twitter services and many of those messages contain feedback to companies about their services. One company that recognizes this unused source of information is Netflix. That is why Netflix initiated the development of a system that lets them respond to the millions of Twitter and Netflix users that are acting as sensors and reporting all types of user visible outages. This system enhances the feedback loop between Netflix and its customers by increasing the amount of customer feedback that Netflix receives and reducing the time it takes for Netflix to …

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