Efficient regularized least-squares algorithms for conditional ranking on relational data
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Bernard De Baets | Tapio Pahikkala | Antti Airola | Michiel Stock | Willem Waegeman | B. Baets | Michiel Stock | T. Pahikkala | W. Waegeman | A. Airola
[1] Elena Marchiori,et al. Gaussian interaction profile kernels for predicting drug-target interaction , 2011, Bioinform..
[2] Alexander Gammerman,et al. Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.
[3] Samy Bengio,et al. A Discriminative Kernel-Based Approach to Rank Images from Text Queries , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Thomas Hofmann,et al. Unifying collaborative and content-based filtering , 2004, ICML.
[5] Richard Simon,et al. Bias in error estimation when using cross-validation for model selection , 2006, BMC Bioinformatics.
[6] S. R. Searle,et al. On Deriving the Inverse of a Sum of Matrices , 1981 .
[7] Carla D. Moravitz Martin,et al. Shifted Kronecker Product Systems , 2006, SIAM J. Matrix Anal. Appl..
[8] Yunming Ye,et al. MultiRank: co-ranking for objects and relations in multi-relational data , 2011, KDD.
[9] Tapio Pahikkala,et al. An efficient algorithm for learning to rank from preference graphs , 2009, Machine Learning.
[10] Heikki Mannila,et al. Relational link-based ranking , 2004, VLDB.
[11] Bernard De Baets,et al. From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification , 2010, BMC Bioinformatics.
[12] Yoshihiro Yamanishi,et al. propagation: A fast semisupervised learning algorithm for link prediction , 2009 .
[13] Yin Yang,et al. Query by document , 2009, WSDM '09.
[14] Tapio Salakoski,et al. Learning monadic and dyadic relations : three case studies in systems biology , 2012 .
[15] Thomas Hofmann,et al. Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..
[16] Bernard De Baets,et al. Cyclic Evaluation of Transitivity of Reciprocal Relations , 2006, Soc. Choice Welf..
[17] Bernhard Schölkopf,et al. Joint Kernel Maps , 2005, IWANN.
[18] Andreas Fischer,et al. Pairwise support vector machines and their application to large scale problems , 2012, J. Mach. Learn. Res..
[19] Henk A. van der Vorst,et al. Bi-CGSTAB: A Fast and Smoothly Converging Variant of Bi-CG for the Solution of Nonsymmetric Linear Systems , 1992, SIAM J. Sci. Comput..
[20] C. Loan. The ubiquitous Kronecker product , 2000 .
[21] Shivani Agarwal,et al. Ranking on graph data , 2006, ICML.
[22] Tommi S. Jaakkola,et al. Maximum-Margin Matrix Factorization , 2004, NIPS.
[23] Tapio Salakoski,et al. A Kernel-Based Framework for Learning Graded Relations From Data , 2011, IEEE Transactions on Fuzzy Systems.
[24] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[25] Charles R. Johnson,et al. Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.
[26] Yoram Singer,et al. An Efficient Boosting Algorithm for Combining Preferences by , 2013 .
[27] S. Yau. Mathematics and its applications , 2002 .
[28] Tapio Salakoski,et al. On Learning and Cross-Validation with Decomposed Nyström Approximation of Kernel Matrix , 2011, Neural Processing Letters.
[29] Christopher D. Manning,et al. Using Feature Conjunctions Across Examples for Learning Pairwise Classifiers , 2004, ECML.
[30] William Stafford Noble,et al. Kernel methods for predicting protein-protein interactions , 2005, ISMB.
[31] Eyke Hüllermeier,et al. Bipartite Ranking through Minimization of Univariate Loss , 2011, ICML.
[32] Charles Elkan,et al. Predicting labels for dyadic data , 2010, Data Mining and Knowledge Discovery.
[33] Tie-Yan Liu,et al. Listwise approach to learning to rank: theory and algorithm , 2008, ICML '08.
[34] Tie-Yan Liu,et al. Learning to rank for information retrieval , 2009, SIGIR.
[35] Kristian Kersting,et al. Learning Preferences with Hidden Common Cause Relations , 2009, ECML/PKDD.
[36] Eyke Hüllermeier,et al. Label ranking by learning pairwise preferences , 2008, Artif. Intell..
[37] Phil Culverhouse,et al. Time to automate identification , 2010, Nature.
[38] Tao Qin,et al. Learning to rank relational objects and its application to web search , 2008, WWW.
[39] S. Sathiya Keerthi,et al. Efficient algorithms for ranking with SVMs , 2010, Information Retrieval.
[40] Thorsten Joachims,et al. Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes , 2010, ECML/PKDD.
[41] Thorsten Joachims,et al. Optimizing search engines using clickthrough data , 2002, KDD.
[42] Len Fisher,et al. Rock, paper, scissors : game theory in everyday life , 2008 .
[43] Charles R. Johnson,et al. Topics in Matrix Analysis , 1991 .
[44] Eyke Hüllermeier,et al. Binary Decomposition Methods for Multipartite Ranking , 2009, ECML/PKDD.
[45] Alexander J. Smola,et al. Learning Graph Matching , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[46] G. Wahba,et al. Some results on Tchebycheffian spline functions , 1971 .
[47] Tie-Yan Liu,et al. Adapting ranking SVM to document retrieval , 2006, SIGIR.
[48] Tapio Salakoski,et al. Learning intransitive reciprocal relations with kernel methods , 2010, Eur. J. Oper. Res..
[49] William I. Gasarch. Review of rock, paper, scissors: game theory for everyday life by Len Fisher (Basic Books, 2008) , 2009, SIGA.
[50] Tapio Salakoski,et al. Conditional Ranking on Relational Data , 2010, ECML/PKDD.
[51] Jason Weston,et al. Protein ranking: from local to global structure in the protein similarity network. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[52] Filip Radlinski,et al. A support vector method for optimizing average precision , 2007, SIGIR.
[53] Yoshihiro Yamanishi,et al. On Pairwise Kernels: An Efficient Alternative and Generalization Analysis , 2009, PAKDD.
[54] Tapio Salakoski,et al. Training linear ranking SVMs in linearithmic time using red-black trees , 2010, Pattern Recognit. Lett..
[55] Yoshihiro Yamanishi,et al. Protein network inference from multiple genomic data: a supervised approach , 2004, ISMB/ECCB.
[56] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[57] Lorenzo Rosasco,et al. Learning from Examples as an Inverse Problem , 2005, J. Mach. Learn. Res..
[58] Hisashi Kashima,et al. Fast and Scalable Algorithms for Semi-supervised Link Prediction on Static and Dynamic Graphs , 2010, ECML/PKDD.
[59] H. Engl,et al. Regularization of Inverse Problems , 1996 .
[60] Michèle Sebag,et al. Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III , 2010 .
[61] Eugene Galanter,et al. Handbook of mathematical psychology: I. , 1963 .
[62] Pierre Geurts,et al. Inferring biological networks with output kernel trees , 2007, BMC Bioinformatics.
[63] Thorsten Joachims,et al. Training linear SVMs in linear time , 2006, KDD '06.
[64] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[65] Ingo Steinwart,et al. On the Influence of the Kernel on the Consistency of Support Vector Machines , 2002, J. Mach. Learn. Res..
[66] Tie-Yan Liu,et al. Learning to Rank for Information Retrieval , 2011 .
[67] Tomaso A. Poggio,et al. Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..
[68] William Stafford Noble,et al. A new pairwise kernel for biological network inference with support vector machines , 2007, BMC Bioinformatics.