• A multi-scale framework for graph based machine learning problems 

    Shin, Donghyuk; 0000-0001-8687-0258 (2017-05)
    Graph data have become essential in representing and modeling relationships between entities and complex network structures in various domains such as social networks and recommender systems. As a main contributor of the ...
  • Active visual category learning 

    Vijayanarasimhan, Sudheendra (2011-05)
    Visual recognition research develops algorithms and representations to autonomously recognize visual entities such as objects, actions, and attributes. The traditional protocol involves manually collecting training image ...
  • Adaptive trading agent strategies using market experience 

    Pardoe, David Merrill (2011-05)
    Along with the growth of electronic commerce has come an interest in developing autonomous trading agents. Often, such agents must interact directly with other market participants, and so the behavior of these participants ...
  • All-by-all discovery of conserved protein complexes by deep proteome fractionation 

    Borgeson, Blake Charles; 0000-0003-1776-5256 (2016-08)
    Stable assemblies of proteins, known as protein complexes, execute a large fraction of cellular processes required to sustain life. A functional and mechanistic understanding of these assemblies will provide a more ...
  • The application of machine learning methods in software verification and validation 

    Phuc, Nguyen Vinh, 1955- (2010-08)
    Machine learning methods have been employed in data mining to discover useful, valid, and beneficial patterns for various applications of which, the domain encompasses areas in business, medicine, agriculture, census, and ...
  • Automated domain analysis and transfer learning in general game playing 

    Kuhlmann, Gregory John (2010-08)
    Creating programs that can play games such as chess, checkers, and backgammon, at a high level has long been a challenge and benchmark for AI. Computer game playing is arguably one of AI's biggest success stories. ...
  • Autonomous qualitative learning of distinctions and actions in a developing agent 

    Mugan, Jonathan William (2010-08)
    How can an agent bootstrap up from a pixel-level representation to autonomously learn high-level states and actions using only domain general knowledge? This thesis attacks a piece of this problem and assumes that an agent ...
  • Autonomous sensor and action model learning for mobile robots 

    Stronger, Daniel Adam (2008-08)
    Autonomous mobile robots have the potential to be extremely beneficial to society due to their ability to perform tasks that are difficult or dangerous for humans. These robots will necessarily interact with their environment ...
  • Autonomous trading in modern electricity markets 

    Urieli, Daniel (2015-12)
    The smart grid is an electricity grid augmented with digital technologies that automate the management of electricity delivery. The smart grid is envisioned to be a main enabler of sustainable, clean, efficient, reliable, ...
  • Breast cancer prediction using machine learning algorithm 

    Yu, Mengjie (2017-06-30)
    Breast cancer, mostly occurring in women, is the mostly frequently diagnosed cancer. Early detection based on phenotype and genotype features can greatly increases the chances for successful treatment. In this report, ...
  • Catweetegories : machine learning to organize your Twitter stream 

    Simoes, Christopher Francis (2013-12)
    We want to create a web service that will help users better organize the flood of tweets they receive every day by using machine learning. This was done by experimenting with ways to manually classify training sets of ...
  • Creating diverse ensemble classifiers to reduce supervision 

    Melville, Prem Noel (2005)
    Ensemble methods like Bagging and Boosting which combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the members of an ensemble is known to be an ...
  • Crunch the market : a Big Data approach to trading system optimization 

    Mauldin, Timothy Allan (2013-12)
    Due to the size of data needed, running software to analyze and tuning intraday trading strategies can take large amounts of time away from analysts, who would like to be able to evaluate strategies and optimize strategy ...
  • Data driven analysis of fast oxide ion diffusion in solid oxide fuel cell cathodes 

    Miller, Alexander Scot; 0000-0002-3991-5262 (2015-08)
    The goal of this study was to determine whether atomic-scale features (related to composition and crystal structure) of perovskite and perovskite-related materials could be used to predict fast oxide ion diffusion for Solid ...
  • Data mining techniques for classifying RNA folding structures 

    Kim, Vince (2016-08)
    RNA is a crucial biological molecule that is critical for protein synthesis. Significant research has been done on folding algorithms for RNA, in particular the 16S rRNA of bacteria and archaea. Rather than modifying current ...
  • Developing a real-time freeway incident detection model using machine learning techniques 

    Motamed, Moggan; 0000-0002-5205-2641 (2016-05)
    Real-time incident detection on freeways plays an important part in any modern traffic management operation by maximizing road system performance. The US Department of Transportation (US-DOT) estimates that over half of ...
  • Dirty statistical models 

    Jalali, Ali, 1982- (2012-05)
    In fields across science and engineering, we are increasingly faced with problems where the number of variables or features we need to estimate is much larger than the number of observations. Under such high-dimensional ...
  • Discriminative object categorization with external semantic knowledge 

    Hwang, Sung Ju (2013-08)
    Visual object category recognition is one of the most challenging problems in computer vision. Even assuming that we can obtain a near-perfect instance level representation with the advances in visual input devices and ...
  • Efficient non-convex algorithms for large-scale learning problems 

    Park, Dohyung (2016-12)
    The emergence of modern large-scale datasets has led to a huge interest in the problem of learning hidden complex structures. Not only can models from such structures fit the datasets, they also have good generalization ...
  • Exploiting structure in large-scale optimization for machine learning 

    Hsieh, Cho-Jui; 0000-0003-1624-1733 (2015-08)
    With an immense growth of data, there is a great need for solving large-scale machine learning problems. Classical optimization algorithms usually cannot scale up due to huge amount of data and/or model parameters. In this ...