A Machine Learning Approach to Technology Enhanced Learning

[1]  Alexandros Karatzoglou,et al.  Maximum Margin Code Recommendation , 2009 .

[2]  Mira Mezini,et al.  Learning from examples to improve code completion systems , 2009, ESEC/SIGSOFT FSE.

[3]  Deepak Agarwal,et al.  Regression-based latent factor models , 2009, KDD.

[4]  Rong Pan,et al.  Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering , 2009, KDD.

[5]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[6]  Kilian Q. Weinberger,et al.  Feature hashing for large scale multitask learning , 2009, ICML '09.

[7]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[8]  Alexander J. Smola,et al.  Adaptive collaborative filtering , 2008, RecSys '08.

[9]  Alexander J. Smola,et al.  Improving maximum margin matrix factorization , 2008, Machine Learning.

[10]  Geoffrey J. Gordon,et al.  A Unified View of Matrix Factorization Models , 2008, ECML/PKDD.

[11]  Charles Elkan,et al.  Learning classifiers from only positive and unlabeled data , 2008, KDD.

[12]  N. Schraudolph,et al.  A quasi-Newton approach to non-smooth convex optimization , 2008, ICML '08.

[13]  Oliver Brdiczka,et al.  Detecting small group activities from multimodal observations , 2009, Applied Intelligence.

[14]  Alexander J. Smola,et al.  Maximum Margin Matrix Factorization for Collaborative Ranking , 2007 .

[15]  Alexander J. Smola,et al.  Bundle Methods for Machine Learning , 2007, NIPS.

[16]  Wei Chu,et al.  Gaussian Process Models for Link Analysis and Transfer Learning , 2007, NIPS.

[17]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[18]  Domonkos Tikk,et al.  Major components of the gravity recommendation system , 2007, SKDD.

[19]  Chris H. Q. Ding,et al.  Binary Matrix Factorization with Applications , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[20]  Andreas Zeller,et al.  Detecting object usage anomalies , 2007, ESEC-FSE '07.

[21]  Andrei Z. Broder,et al.  Estimating rates of rare events at multiple resolutions , 2007, KDD '07.

[22]  Max Mühlhäuser,et al.  Automatically Assessing the Post Quality in Online Discussions on Software , 2007, ACL.

[23]  Iryna Gurevych,et al.  Predicting the perceived quality of web forum posts , 2007 .

[24]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[25]  Max Mühlhäuser,et al.  Darmstadt Knowledge Processing Repository Based on UIMA , 2007 .

[26]  Zoubin Ghahramani,et al.  Modeling Dyadic Data with Binary Latent Factors , 2006, NIPS.

[27]  Francis R. Bach,et al.  Low-rank matrix factorization with attributes , 2006, ArXiv.

[28]  Mira Mezini,et al.  FrUiT: IDE support for framework understanding , 2006, ETX.

[29]  Michael Eichberg,et al.  The SEXTANT Software Exploration Tool , 2006, IEEE Transactions on Software Engineering.

[30]  Natalia Stash,et al.  The Design of AHA! , 2006, HYPERTEXT '06.

[31]  Ingo Mierswa,et al.  YALE: rapid prototyping for complex data mining tasks , 2006, KDD '06.

[32]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[33]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[34]  Soo-Min Kim,et al.  Automatically Assessing Review Helpfulness , 2006, EMNLP.

[35]  Hans-Peter Kriegel,et al.  Collaborative ordinal regression , 2006, ICML.

[36]  Jian Pei,et al.  MAPO: mining API usages from open source repositories , 2006, MSR '06.

[37]  Jakob Nielsen,et al.  Prioritizing Web Usability , 2006 .

[38]  Wei Chu,et al.  Gaussian Processes for Ordinal Regression , 2005, J. Mach. Learn. Res..

[39]  Thomas Hofmann,et al.  Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..

[40]  Benjamin Livshits,et al.  DynaMine: finding common error patterns by mining software revision histories , 2005, ESEC/FSE-13.

[41]  Zhenmin Li,et al.  PR-Miner: automatically extracting implicit programming rules and detecting violations in large software code , 2005, ESEC/FSE-13.

[42]  Nathan Srebro,et al.  Fast maximum margin matrix factorization for collaborative prediction , 2005, ICML.

[43]  Adi Shraibman,et al.  Rank, Trace-Norm and Max-Norm , 2005, COLT.

[44]  Rastislav Bodík,et al.  Jungloid mining: helping to navigate the API jungle , 2005, PLDI '05.

[45]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[46]  R. Holmes,et al.  Using structural context to recommend source code examples , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..

[47]  Kurt Hornik,et al.  kernlab - An S4 Package for Kernel Methods in R , 2004 .

[48]  M. Chodorow,et al.  BEYOND ESSAY LENGTH: EVALUATING E-RATER®'S PERFORMANCE ON TOEFL® ESSAYS , 2004 .

[49]  Paul Resnick,et al.  Slash(dot) and burn: distributed moderation in a large online conversation space , 2004, CHI.

[50]  Yoram Singer,et al.  Logistic Regression, AdaBoost and Bregman Distances , 2000, Machine Learning.

[51]  Subrata Mitra,et al.  Design by framework completion , 1996, Automated Software Engineering.

[52]  Yoram Singer,et al.  Smooth e-Intensive Regression by Loss Symmetrization , 2005, COLT.

[53]  Ben Taskar,et al.  Max-Margin Markov Networks , 2003, NIPS.

[54]  Bing Liu,et al.  Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression , 2003, ICML.

[55]  Tommi S. Jaakkola,et al.  Weighted Low-Rank Approximations , 2003, ICML.

[56]  Salvatore Valenti,et al.  An Overview of Current Research on Automated Essay Grading , 2003, J. Inf. Technol. Educ..

[57]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[58]  John R. Anderson,et al.  What role do cognitive architectures play in intelligent tutoring systems , 2001 .

[59]  Gerhard Fischer,et al.  Integrating active information delivery and reuse repository systems , 2000, SIGSOFT '00/FSE-8.

[60]  Forrest Shull,et al.  Investigating Reading Techniques for Object-Oriented Framework Learning , 2000, IEEE Trans. Software Eng..

[61]  Amir Michail,et al.  Data mining library reuse patterns using generalized association rules , 2000, Proceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium.

[62]  Gregory Butler,et al.  A framework for framework documentation , 2000, CSUR.

[63]  Ralf Herbrich,et al.  Large margin rank boundaries for ordinal regression , 2000 .

[64]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[65]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[66]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[67]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[68]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[69]  David A. Hull.  Improving text retrieval for the routing problem using latent semantic indexing , 1994, SIGIR '94.

[70]  Ralph E. Johnson,et al.  Patterns Generate Architectures , 1994, ECOOP.

[71]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[72]  Ralph E. Johnson.  Documenting frameworks using patterns , 1992, OOPSLA.

[73]  Scott Henninger,et al.  Retrieving software objects in an example-based programming environment , 1991, SIGIR 1991.

[74]  Ian M. Holland,et al.  Contracts: specifying behavioral compositions in object-oriented systems , 1990, OOPSLA/ECOOP '90.

[75]  John R. Anderson,et al.  The TEACHER'S APPRENTICE: Designing an intelligent authoring system for high school mathematics , 1987 .

[76]  William B. Frakes,et al.  Software reuse through information retrieval , 1986, SIGF.

[77]  Robert B. Miller,et al.  Response time in man-computer conversational transactions , 1899, AFIPS Fall Joint Computing Conference.