@article {333,
	title = {Word combination kernel for text categorization},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {202 - 211},
	abstract = {We 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.},
	keywords = {Kernel methods, Machine learning, Support vector machine, Text classification, Word-combination kernel},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866439932\&partnerID=40\&md5=8649dfc8c1ee23ca70f2b137a74a19e4},
	author = {Zhang, L. and Hu, X. and Qin, S.}
}
@article {188,
	title = {Query-by-multiple-examples using support vector machines},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2009},
	pages = {202 - 210},
	abstract = {We 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.},
	keywords = {Information retrieval, Machine learning, Supportvector machine, Text classification},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953247724\&partnerID=40\&md5=8fea0823a5d70e478bf228f315a5ef89},
	author = {Zhang, D. and Lee, W.S.}
}
