Selective sampling techniques for feedback-based data retrieval

Brinker K (2004) Active learning of label ranking functions. In: Proceedings of the International Conference on Machine Learning (ICML’04)

Bruno N, Gravano L, Marian A (2002) Evaluating top-k queries over web-accessible databases. In: Proceedings of the 18th International Conference on Data Engineering (ICDE’02)

Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2: 121–167

Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2004) Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning (ICML’04)

Cao Y, Xu J, Liu T-Y, Li H, Huang Y, Hon H-W (2006) Adapting ranking svm to document retrieval. In: Proceedings of the 29th annual international ACM SIGIR Conference on Information Retrieval (SIGIR’06)

Carey MJ, Kossmann D (1997) On saying “enough already!” in SQL. In: SIGMOD 1997, Proceedings ACM SIGMOD international conference on management of data, May 13–15, 1997, Tucson, Arizona, USA, pp 219–230

Carey MJ, Kossmann D (1998) Reducing the braking distance of an SQL query engine. In: VLDB’98, Proceedings of 24th international conference on very large data bases, August 24–27, New York City, New York, USA, pp 158–169

Cauwenberghs G, Poggio T (2000) Incremental and decremental support vector machine learning. In: Proceedings of advances in Neural Information Processing Systems (NIPS’00), pp 409–415

Chang E, Tong S (2001) Support vector machine active learning for image retrieval. In: Proceedings of the 9th ACM international conference on multimedia 2001, Ottawa, Canada

Chang KC-C, Hwang S-W (2002) Minimal probing: supporting expensive predicates for top-k queries. In: SIGMOD 2002

Chaudhuri S, Gravano L (1999) Evaluating top-k selection queries. In: VLDB’99, Proceedings of 25th international conference on very large data bases, September 7–10, Edinburgh, Scotland, UK, pp 397–410

Christianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge, UK

Cohen WW, Schapire RE, Singer Y (1998) Learning to order things. In: Proceedings of the 1997 conference on advances in Neural Information Processing Systems (NIPS’98)

Domeniconi C, Gunopulos D (2001) Incremental support vector machine construction. In: Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM’01)

Fagin R, Lote A, Naor M (2001) Optimal aggregation algorithms for middleware. In: Proceedings of the 20th ACM SIGACT-SIGMOD-SIGART symposium on Principles of Database Systems (PODS’01)

Fung G, Mangasarian OL (2002) Incremental support vector machine classification. In: Proceedings of the second SIAM International Conference on Data Mining (SDM’02)

Furnkranz J, Hullermeier E (2003) Pairwise preference learning and ranking. In: Proceedings of the 14th European Conference on Machine Learning (ECML’03)

Har-Peled S, Roth D, Zimak D (2002) Constraint classification: a new approach to multiclass classification and ranking. In: Advances in Neural Information Processing Systems (NIPS’02), vol 15. MIT Press, Cambridge

Herbrich, R, Graepel, T, Obermayer, K (eds) (2000) Large margin rank boundaries for ordinal regression. MIT Press, Cambridge

Hersh W, Buckley C, Leone TJ, Hickam D (1994) OHSUMED: an interactive retrieval evaluation and new large test collection for research. In: Proceedings of the 17th Annual ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’94)

Hristidis V, Koudas N, Papakonstantinou Y (2001) PREFER: a system for the efficient execution of multi-parametric ranked queries. Proceedings ACM SIGMOD international conference on management of data

Joachims T (2002) Optimizing search engines using clickthrough data. In: Proceedings of the ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD’02)

Joachims T (2006) Training linear svms in linear time. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD’06)

Letor: Learning to rank for information retrieval. http://research.microsoft.com/users/LETOR/

Li C, Chang KC-C, Ilyas IF, Song S (2005) RankSQL: query algebra and optimization for relational top-k queries. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’05)

Nguyen TQ, Heo JS, Lee JH, Kim YR, Whang KY (2008) Query expansion using augmented terms in an extended boolean model. J Comput Sci Eng 2: 26–43

Qin T, Liu T-Y, Lai W, Zhang X-D, Wang D-S, Li H (2007) Ranking with multiple hyperplanes. In: Proceedings of the 30th annual international ACM SIGIR conference on Information Retrieval (SIGIR’07)

Radlinski F, Joachims T (2005) Query chains: learning to rank from implicit feedback. In: Proceedings of the 11th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD’05)

Schohn G, Cohn D (2000) Less is more: active learning with support vector machines. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML’00), pp 839–846

SVM-light. http://svmlight.joachims.org/

Syed N, Liu H, Sung K (1999) Incremental learning with support vector machines. In: Proceedings of workshop on support vector machines at International Joint Conference on Artificial Intelligence, Stockholm (IJCAI’99)

Tong S, Koller D (2000) Support vector machine active learning with applications to text classification. In: Proceedings of the seventeenth International Conference on Machine Learning (ICML’00), pp 999–1006

Vapnik V (1998) Statistical learning theory. Wiley, New York

Xiao R, Wang J, Zhang F (2000) An approach to incremental SVM learning algorithm. In: Proceedings of the 12th IEEE international conference on tools with artificial intelligence

Xu J, Li H (2007) AdaRank: a boosting algorithm for information retrieval. In: Proceedings of the 30th annual international ACM SIGIR conference on Information Retrieval (SIGIR’07)

Yu H (2005) SVM selective sampling for ranking with application to data retrieval. In: Proceedings of the international conference on Knowledge Discovery and Data Mining (KDD’05)

Yu H, Hwang S, Chang KC-C (2005) RankFP: a framework for supporting rank formulation and processing. In: Proceedings of the international conference on Data Engineering (ICDE’05), University of Iowa

Yu H, Hwang S-W, Chang KC-C (2007) Enabling soft queries for data retrieval. Inf Syst 32(4): 560–574