MACHINE LEARNING APPROACH FOR DETERMINING DOCUMENT RELEVANCE FOR SEARCHING LARGE-SCALE COLLECTION OF ELECTRONIC DOCUMENT,申请号JP20050004310-传众专利搜索

MACHINE LEARNING APPROACH FOR DETERMINING DOCUMENT RELEVANCE FOR SEARCHING LARGE-SCALE COLLECTION OF ELECTRONIC DOCUMENT

申请公布号：JP2005222532(A)

申请号：JP20050004310

申请日期：2005.01.11

申请公布日期：2005.08.18

申请人：

MICROSOFT CORP

发明人：CHEN HARR;CHANDRASEKAR RAMAN;CORSTON SIMON H

分类号：G06F17/30;G06N3/08;(IPC1-7):G06N3/08

主分类号：G06F17/30

摘要：PROBLEM TO BE SOLVED: To provide a system and methodology that applies automated learning procedures for determining document relevance and assisting information retrieval activities. SOLUTION: The system includes a storage component that receives a set of human selected items to be employed as positive test cases of highly relevant documents. A training component trains at least one classifier with the human selected items as positive test cases and one or more other items as negative test cases in order to provide a query-independent model, wherein the other items are selected by a statistical search, for example. COPYRIGHT: (C)2005,JPO&NCIPI