Machine Learning: Discriminative and Generative

Meila, Marina
January 2006
Mathematical Intelligencer;Winter2006, Vol. 28 Issue 1, p67
Book Review
The article reviews the book "Machine Learning Discriminative and Generative," by Tony Jebara.


Related Articles

  • The New Cartographers. Kite, Buddy // Esquire;Dec2008, Vol. 150 Issue 6, p168 

    The article focuses on the work of four innovative cartographers. Technology has allowed the field to move away from traditional cartography and toward the building dynamic maps that are more about people than places. Laura Kurgan, an architect and director of the Spatial Information Design Lab,...

  • Stephen Marsland: Machine learning. An algorithmic perspective. Mildenberger, Thoralf // Statistical Papers;May2014, Vol. 55 Issue 2, p575 

    No abstract available.

  • Introduction to the Special Issue on Kernel Methods. Cristianini, Nello; Shawe-Taylor, John; Williamson, Robert C. // Journal of Machine Learning Research;Spring2002, Vol. 2 Issue 2, p95 

    Introduces a series of articles published in the March 2002 issue of the 'Journal of Machine Learning,' Volume II.

  • Enzyme classification with peptide programs: a comparative study.  // BMC Bioinformatics;2009, Vol. 10, p231 

    Background: Efficient and accurate prediction of protein function from sequence is one of the standing problems in Biology. The generalised use of sequence alignments for inferring function promotes the propagation of errors, and there are limits to its applicability. Several machine learning...

  • Accurate discrimination of conserved coding and non-coding regions through multiple indicators of evolutionary dynamics.  // BMC Bioinformatics;2009, Vol. 10, p282 

    Background: The conservation of sequences between related genomes has long been recognised as an indication of functional significance and recognition of sequence homology is one of the principal approaches used in the annotation of newly sequenced genomes. In the context of recent findings that...

  • Does machine learning really work? Mitchell, Tom M. // AI Magazine;Fall97, Vol. 18 Issue 3, p11 

    Focuses on the accomplishments in machine learning based on the keynote talk which was presented at the Thirteenth National Conference on Artificial Intelligence. How machine learning has developed from a field of laboratory demonstration to a field of significant commercial value;...

  • A Bayesian framework for integrating genomic data to aid function prediction. Bridson, Chris; Morris, Richard J. // BMC Systems Biology;2007 Supplement 1, Vol. 1, pP62 

    An abstract of the article "A Bayesian framework for integrating genomic data to aid function prediction," by Chris Bridson and Richard J. Morris is presented.

  • Genetic Algorithms in Search, Optimization and Machine Learning (Book Review). Ryan, Jennifer // ORSA Journal on Computing;Spring91, Vol. 3 Issue 2, p176 

    Reviews the book 'Genetic Algorithms in Search, Optimization and Machine Learning,' by D.E. Goldberg.

  • Truth from Trash (Book). Michie, Jean Hayes // AI Magazine;Winter2001, Vol. 22 Issue 4, p145 

    Reviews the book 'Truth from Trash: How Learning Makes Sense,' by Chris Thornton.


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics