Skip to main content

and
Your search also matched 15 preview-only Content is preview-only when you or your institution have not yet subscribed to it.

By making our abstracts and previews universally accessible we help you purchase only the content that is relevant to you.
results, e.g.

Class-Driven Color Transformation for Semantic Labeling

Include preview-only content
  1. Chapter and Conference Paper

    Discriminative Interpolation for Classification of Functional Data

    The modus operandi for machine learning is to represent data as feature vectors and then proceed with training algorithms that seek to optimally partition the feature space

    Rana Haber, Anand Rangarajan in Machine Learning and Knowledge Discovery i… (2015)

  2. Chapter and Conference Paper

    Spá: A Web-Based Viewer for Text Mining in Evidence Based Medicine

    Summarizing the evidence about medical interventions is an immense undertaking, in part because unstructured Portable Document Format (PDF) documents remain the main vehicle for disseminating scientific findin...

    J. Kuiper, I. J. Marshall, B. C. Wallace in Machine Learning and Knowledge Discovery i… (2014)

  3. Chapter and Conference Paper

    Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries

    How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity ha...

    Flavio Figueiredo, Jussara M. Almeida in Machine Learning and Knowledge Discovery i… (2014)

  4. Chapter and Conference Paper

    Students, Teachers, Exams and MOOCs: Predicting and Optimizing Attainment in Web-Based Education Using a Probabilistic Graphical Model

    We propose a probabilistic graphical model for predicting student attainment in web-based education. We empirically evaluate our model on a crowdsourced dataset with students and teachers; Teachers prepared le...

    Bar Shalem, Yoram Bachrach, John Guiver in Machine Learning and Knowledge Discovery i… (2014)

  5. Chapter and Conference Paper

    Decision-Theoretic Sparsification for Gaussian Process Preference Learning

    We propose a decision-theoretic sparsification method for Gaussian process preference learning. This method overcomes the loss-insensitive nature of popular sparsification approaches such as the Informative Ve...

    M. Ehsan Abbasnejad, Edwin V. Bonilla in Machine Learning and Knowledge Discovery i… (2013)