Material Detail

Density Ratio Estimation in Machine Learning

Density Ratio Estimation in Machine Learning

This video was recorded at BBCI Workshop: Advances in Neurotechnologies, Berlin 2012. In statistical machine learning, avoiding density estimation is essential because it is often more difficult than solving a target machine learning problem itself. This is often referred to as Vapnik's principle, and the support vector machine is one of the successful realizations of this principle. Following this spirit, a new machine learning framework based on the ratio of probability density functions has been introduced recently. This density-ratio framework includes various important machine learning tasks such as transfer learning, outlier detection, feature selection, clustering, and conditional density estimation. All these tasks can be effectively and efficiently solved in a unified manner by... Show More
Rate

Quality

  • Editor Reviews
  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.
hidden