When.com Web Search

Search results

  1. Results From The WOW.Com Content Network
  2. Learning to rank - Wikipedia

    en.wikipedia.org/wiki/Learning_to_rank

    In this case, the learning-to-rank problem is approximated by a classification problem — learning a binary classifier (,) that can tell which document is better in a given pair of documents. The classifier shall take two documents as its input and the goal is to minimize a loss function L ( h ; x u , x v , y u , v ) {\displaystyle L(h;x_{u},x ...

  3. Ranking SVM - Wikipedia

    en.wikipedia.org/wiki/Ranking_SVM

    In machine learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems (via learning to rank). The ranking SVM algorithm was published by Thorsten Joachims in 2002. [1] The original purpose of the algorithm was to improve the performance of an internet search engine.

  4. Jianchang Mao - Wikipedia

    en.wikipedia.org/wiki/Jianchang_Mao

    Mao grew up in Zhejiang, China.He got his bachelor's degree in Physics and master's degree in Electronics from East China Normal University, Shanghai, China.He studied artificial neural networks, pattern recognition and machine learning at Michigan State University under the supervision of University Distinguished Professor Anil K. Jain and earned a Ph.D. in Computer Science in 1994.

  5. Ranking (information retrieval) - Wikipedia

    en.wikipedia.org/wiki/Ranking_(information...

    Ranking of query is one of the fundamental problems in information retrieval (IR), [1] the scientific/engineering discipline behind search engines. [2] Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user.

  6. Preference learning - Wikipedia

    en.wikipedia.org/wiki/Preference_learning

    Preference learning can be used in ranking search results according to feedback of user preference. Given a query and a set of documents, a learning model is used to find the ranking of documents corresponding to the relevance with this query. More discussions on research in this field can be found in Tie-Yan Liu's survey paper. [6]

  7. File:RelativeRankLearning2.pdf - Wikipedia

    en.wikipedia.org/wiki/File:RelativeRankLearning2.pdf

    English: Learning in the partial-information sequential search paradigm. The numbers display the expected values of applicants based on their relative rank (out of m total applicants seen so far) at various points in the search. Expectations are calculated based on the case when their values are uniformly distributed between 0 and 1.

  8. Digital textbook - Wikipedia

    en.wikipedia.org/wiki/Digital_Textbook

    A digital textbook is a digital book or e-book intended to serve as the text for a class. Digital textbooks may also be known as e-textbooks or e-texts.Digital textbooks are a major component of technology-based education reform.

  9. Talk:Learning to rank - Wikipedia

    en.wikipedia.org/wiki/Talk:Learning_to_rank

    The FRank description says "Based on RankNet". I would change it to "Motivated by RankNet". The model itself is nothing like RankNet. RankNet is a neural network.