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Filtering by: Creator Riedl, Helmut W. Remove constraint Creator: Riedl, Helmut W. Degree Field Computer Science Remove constraint Degree Field: Computer Science

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  • Machine Learning Techniques to Model Species Interactions and Distributions Under Imperfect Detection

    Creator:
    Seo, Eugene
    Abstract:
    Ecological domains seeking to understand the environment and the behavior of species have received little attention in machine learning (ML), despite the fact that environmental changes have a significant impact on humans as well as ecosystems. Some ecological problems can be formulated similarly to other common ML applications, but there...
    Resource Type:
    Dissertation
    Full Text:
    been a great inspiration to me. I must also thank Dr. Tyler A. Hallman, Dr. W. Douglas Robinson, and

  • A collaborative filtering algorithm and evaluation metric that accurately model the user experience

    Creator:
    McLaughlin, Matthew
    Abstract:
    "Collaborative filtering algorithms’ performances have been evaluated using a variety of metrics. These metrics, such as Mean Absolute Error and Precision, have often ignored recommendations for which they do not have data. Ignoring these recommendations has provided numbers which do not accurately represent the user experience. Qualitatively we have seen...
    Resource Type:
    Capstone Project
    Full Text:
    user u, let w(a,u) be the Pearson-Correlation between user a and user u. After computing the

  • Interactive visualization of diversity in multivariate data sets unified across fields of study

    Creator:
    Pham, Tuan N. T.
    Abstract:
    The study of the diversity of multivariate objects shares common characteristics across disciplines, including ecology and organizational management. Nevertheless, experts in these two disciplines have adopted somewhat separate diversity concepts and analysis techniques, limiting the ability of potentially sharing and cross comparing these concerns. Moreover, while complex diversity data may...
    Resource Type:
    Dissertation

  • Contributions to a Java recommender system

    Creator:
    Wang, Yun
    Abstract:
    Identifying the most relevant items in an e-commerce site is becoming more and more difficult nowadays because of the heavy overload of information. A Java Recommender System that uses Collaborative Filtering techniques has been developed to reduce such information overload and even personalize the information to the individual’s preference. The...
    Resource Type:
    Capstone Project
    Full Text:
    all neighbors. The formula is given as follows: ua m i uiuaia ua rrrr w σσ * )(*)( 1

  • Adding context and value to online security ratings by combining heuristics with community based ratings

    Creator:
    Ayyavu, Prashanth
    Abstract:
    The Internet is growing rapidly in terms of websites, users and uses. People use the Internet for reference, shopping, social networking, communications, business and much more. Though the Internet is useful, there are many risks associated with its use, like malicious websites, identity theft, hateful content and fraudulent practices. Online...
    Resource Type:
    Masters Thesis

  • Contributions to Collaborative Filtering Research

    Creator:
    Nong, Shuzhen
    Abstract:
    In this report, I first introduce three areas of interest to collaborative filtering researchers, namely: (a) how to solve the sparsity and scalability problems in recommendation systems; (b) how to rapidly develop and test collaborative filtering algorithms; and (c) how to apply collaborative filtering to unbounded numeric preference data. In...
    Resource Type:
    Capstone Project

  • Designing and understanding information retrieval systems using collaborative filtering in an academic library environment

    Creator:
    Jung, Seikyung
    Abstract:
    Accessing information on the Web has become ingrained into our daily lives, and we seek information from many different sources, including conference and journal publications, personal web pages, and others. Increasingly, web-based information retrieval systems such as web-based search engines, library on-line catalog systems, and subscription-based federated search systems are...
    Resource Type:
    Dissertation

  • On Convex Dimensionality Reduction for Classification

    Creator:
    Tammineni, Tez Deepthi
    Abstract:
    Dimensionality reduction (DR) is an efficient approach to reduce the size of data by capturing the informative intrinsic features and discarding the noise. DR methods can be grouped through a variety of categories, e.g. supervised/ unsupervised, linear/non-linear or parametric/non-parametric. Objective function based methods can be grouped into convex and non...
    Resource Type:
    Capstone Project
    Full Text:
    Representing the trace feasibility region for W . . . . . . . . . . . . . . . . . . . . . 12 3.1 Failed cvx

  • Collaborative filtering with probability and collaborative filtering with privacy

    Creator:
    Yang, Min
    Abstract:
    "Collaborative filtering has seen considerable success in the areas regarding information overload and e-commerce, while the current developed systems are flawed in several respects. Two approaches, the distribution-based algorithm and the blurring profile solution, are proposed to address several outstanding issues. The main findings of this research include: • The...
    Resource Type:
    Capstone Project
    Full Text:
    ajaj j ijiajaj ia vvwvvw vvvvw w 2 , 22 , 2 ,, 2 , (2-1) where jav , and jiv , are the

  • Applying machine learning for prediction, recommendation, and integration

    Creator:
    Bao, Xinlong
    Abstract:
    This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating Learning and Reasoning. FolderPredictor is an intelligent desktop software tool that helps...
    Resource Type:
    Dissertation