Browse Dept. of Computer Science by Subject "learning"

  • Calzada, Daniel (2018-04-25)
    Making reliable preseason batter projections for baseball players is an issue of utmost importance to both teams and fans who seek to infer a player's underlying talent or predict future performance. However, this has ...

    application/pdf

    application/pdfPDF (1MB)
  • Cai, Deng; He, Xiaofei; Han, Jiawei (2006-04)
    Most of the existing learning algorithms take vectors as their input data. A function is then learned in such a vector space for classification, clustering, or dimensionality reduction. However, in some situations, there ...

    application/pdf

    application/pdfPDF (185kB)
  • Garg, Pranav (2015-07-14)
    The problem of synthesizing adequate inductive invariants to prove a program correct lies at the heart of automated program verification. We investigate, herein, learning approaches to synthesize inductive invariants of ...

    application/pdf

    application/pdfPDF (2MB)
  • Kumar, Viraj; Madhusudan, P.; Viswanathan, Mahesh (2006-06)
    Boolean programs with recursion are convenient abstractions of sequential, imperative programs. Recursive state machines (RSM) serve as machine models for Boolean programs and are semantically equivalent to pushdown automata. ...

    application/pdf

    application/pdfPDF (330kB)
  • Kamalnath, Vishnu Nath (2013-08-22)
    This thesis deals with incorporating artificial intelligence into a humanoid robot by making a cognitive model of the learning process. The goal is to “teach” a specialized humanoid robot, the iCub robot, to solve any ...

    application/pdf

    application/pdfPDF (2MB)