01966nas a2200217   4500008004100000245005200041210005200093260000900145300001400154490000700168520130000175653001901475653002101494653002701515653002401542653002801566100001401594700001101608700001201619856011701631       2012                        eng d00aWord combination kernel for text categorization0 aWord combination kernel for text categorization  c2012  a202 - 2110 v103 aWe proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. By restricting the words of a word combination to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the dimensionality this kernel can be reduced significantly compared to the word-sequence kernel. We conducted a series of experiments on the Reuters-21578 dataset and 20 Newsgroups dataset. This kernel consistently achieves better performance than the classical word kernel and word-sequence kernel on the two datasets. We also assessed the impact of word combination length on performance and compared the computing efficiency of this kernel to those of the word kernel and word-sequence kernel.10aKernel methods10aMachine learning10aSupport vector machine10aText classification10aWord-combination kernel1 aZhang, L.1 aHu, X.1 aQin, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866439932&partnerID=40&md5=8649dfc8c1ee23ca70f2b137a74a19e401542nas a2200193   4500008004100000245006100041210005800102260000900160300001400169490000600183520091700189653002601106653002101132653002601153653002401179100001401203700001401217856011701231       2009                        eng d00aQuery-by-multiple-examples using support vector machines0 aQuerybymultipleexamples using support vector machines  c2009  a202 - 2100 v73 aWe identify and explore an Information Retrieval paradigm called Query-By-Multiple-Examples (QBME) where the information need is described not by a set of terms but by a set of documents. Intuitive ideas for QBME include using the centroid of these documents or the well-known Rocchio algorithm to construct the query vector. We consider this problem from the perspective of text classification, and find that a better query vector can be obtained through learning with Support Vector Machines (SVMs). For online queries, we show how SVMs can be learned from one-class examples in linear time. For offline queries, we show how SVMs can be learned from positive and unlabeled examples together in linear or polynomial time, optimising some meaningful multivariate performance measures. The effectiveness and efficiency of the proposed approaches have been confirmed by our experiments on four real-world datasets.10aInformation retrieval10aMachine learning10aSupportvector machine10aText classification1 aZhang, D.1 aLee, W.S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-77953247724&partnerID=40&md5=8fea0823a5d70e478bf228f315a5ef89