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Jude W. Shavlik
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- affiliation: University of Wisconsin-Madison, Madison, USA
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2020 – today
- 2021
- [c109]Raymond J. Mooney, Jude W. Shavlik:
A Recap of Early Work on Theory and Knowledge Refinement. AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering 2021
2010 – 2019
- 2016
- [c108]Ameet Soni, Dileep Viswanathan, Jude W. Shavlik, Sriraam Natarajan:
Learning Relational Dependency Networks for Relation Extraction. ILP 2016: 81-93 - [i5]Dileep Viswanathan, Ameet Soni, Jude W. Shavlik, Sriraam Natarajan:
Learning Relational Dependency Networks for Relation Extraction. CoRR abs/1607.00424 (2016) - 2015
- [j37]Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik:
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases. Mach. Learn. 100(1): 75-100 (2015) - [c107]Raksha Kumaraswamy, Anurag Wazalwar, Tushar Khot, Jude W. Shavlik, Sriraam Natarajan:
Anomaly Detection in Text: The Value of Domain Knowledge. FLAIRS 2015: 225-228 - [i4]Dileep Viswanathan, Anurag Wazalwar, Sriraam Natarajan, Ameet Soni, Jude W. Shavlik:
TAC KBP 2015 : English Slot Filling Track Relational Learning with Expert Advice. TAC 2015 - 2014
- [b2]Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude W. Shavlik:
Boosted Statistical Relational Learners - From Benchmarks to Data-Driven Medicine. Springer Briefs in Computer Science, Springer 2014, ISBN 978-3-319-13643-1, pp. 1-68 - [c106]Tushar Khot, Sriraam Natarajan, Jude W. Shavlik:
Relational One-Class Classification: A Non-Parametric Approach. AAAI 2014: 2453-2459 - [c105]Tushar Khot, Sriraam Natarajan, Jude W. Shavlik:
Classification from One Class of Examples for Relational Domains. StarAI@AAAI 2014 - [c104]Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Ré, Jude W. Shavlik:
Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge. ILP 2014: 92-107 - [c103]Finn Kuusisto, Vítor Santos Costa
, Houssam Nassif, Elizabeth S. Burnside
Support Vector Machines for Differential Prediction. ECML/PKDD (2) 2014: 50-65 - [c102]Xiujun Li, Wei Gao, Jude W. Shavlik:
Detecting Semantic Uncertainty by Learning Hedge Cues in Sentences Using an HMM. SMIR@SIGIR 2014: 30-37 - [c101]Chaitanya Gokhale, Sanjib Das, AnHai Doan, Jeffrey F. Naughton, Narasimhan Rampalli, Jude W. Shavlik, Xiaojin Zhu:
Corleone: hands-off crowdsourcing for entity matching. SIGMOD Conference 2014: 601-612 - 2013
- [c100]Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Ré, Jude W. Shavlik:
Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text. StarAI@AAAI 2013 - [c99]Jie Liu, David Page, Houssam Nassif, Jude W. Shavlik, Peggy L. Peissig, Catherine A. McCarty, Adedayo A. Onitilo, Elizabeth S. Burnside:
Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. AMIA 2013 - [c98]Finn Kuusisto, Inês de Castro Dutra
Using machine learning to identify benign cases with non-definitive biopsy. Healthcom 2013: 283-285 - [c97]Gautam Kunapuli, Phillip Odom, Jude W. Shavlik, Sriraam Natarajan:
Guiding Autonomous Agents to Better Behaviors through Human Advice. ICDM 2013: 409-418 - [c96]Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, Jude W. Shavlik:
Uplift Modeling with ROC: An SRL Case Study. ILP (Late Breaking Papers) 2013: 40-45 - [c95]Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside
Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. ECML/PKDD (3) 2013: 595-611 - [c94]Tushar Khot, Ce Zhang, Jude W. Shavlik, Sriraam Natarajan, Christopher Ré:
Bootstrapping Knowledge Base Acceleration. TREC 2013 - 2012
- [j36]Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik:
Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference. Int. J. Semantic Web Inf. Syst. 8(3): 42-73 (2012) - [j35]Ameet Soni
Probabilistic Ensembles for Improved Inference in protein-Structure Determination. J. Bioinform. Comput. Biol. 10(1) (2012) - [j34]Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann, Jude W. Shavlik:
Gradient-based boosting for statistical relational learning: The relational dependency network case. Mach. Learn. 86(1): 25-56 (2012) - [c93]Timothy W. Clark, William W. Cohen, Lawrence Hunter, Chris J. Lintott, Jude W. Shavlik:
Invited Talks. AAAI Fall Symposium: Discovery Informatics 2012 - [c92]Ce Zhang, Feng Niu, Christopher Ré, Jude W. Shavlik:
Big Data versus the Crowd: Looking for Relationships in All the Right Places. ACL (1) 2012: 825-834 - [c91]Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik:
Scaling Inference for Markov Logic via Dual Decomposition. ICDM 2012: 1032-1037 - [c90]Jude W. Shavlik:
Twenty-Five Years of Combining Symbolic and Numeric Learning. NeSy@AAAI 2012 - [c89]Gautam Kunapuli, Jude W. Shavlik:
Mirror Descent for Metric Learning: A Unified Approach. ECML/PKDD (1) 2012: 859-874 - [c88]Tushar Khot, Siddharth Srivastava, Sriraam Natarajan, Jude W. Shavlik:
Learning Relational Structure for Temporal Relation Extraction. StarAI@UAI 2012 - [c87]Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik:
DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference. VLDS 2012: 25-28 - 2011
- [j33]Feng Niu, Christopher Ré, AnHai Doan, Jude W. Shavlik:
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS. Proc. VLDB Endow. 4(6): 373-384 (2011) - [j32]Faisal Farooq, Balaji Krishnapuram, Rómer Rosales, Shipeng Yu, Jude W. Shavlik, Raju Kucherlapati:
Predictive Models in Personalized Medicine: Neural Information Processing Systems (NIPS), 2010 workshop report. SIGHIT Rec. 1(1): 23-25 (2011) - [c86]Ameet Soni, Jude W. Shavlik:
Probabilistic ensembles for improved inference in protein-structure determination. BCB 2011: 264-273 - [c85]Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik:
Learning Markov Logic Networks via Functional Gradient Boosting. ICDM 2011: 320-329 - [c84]Sriraam Natarajan, Saket Joshi, Prasad Tadepalli
Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach. IJCAI 2011: 1414-1420 - [c83]Trevor Walker, Gautam Kunapuli
Integrating knowledge capture and supervised learning through a human-computer interface. K-CAP 2011: 89-96 - [c82]Gautam Kunapuli, Richard Maclin, Jude W. Shavlik:
Advice Refinement in Knowledge-Based SVMs. NIPS 2011: 1728-1736 - [i3]Feng Niu, Christopher Ré, AnHai Doan, Jude W. Shavlik:
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS. CoRR abs/1104.3216 (2011) - [i2]Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik:
Felix: Scaling Inference for Markov Logic with an Operator-based Approach. CoRR abs/1108.0294 (2011) - 2010
- [j31]Ryan W. Woods, Louis Oliphant, Kazuhiko Shinki, David Page, Jude W. Shavlik, Elizabeth S. Burnside
Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer. J. Digit. Imaging 23(5): 554-561 (2010) - [c81]Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli
Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. StarAI@AAAI 2010 - [c80]Ameet Soni, Craig A. Bingman, Jude W. Shavlik:
Guiding belief propagation using domain knowledge for protein-structure determination. BCB 2010: 285-294 - [c79]Sriraam Natarajan, Gautam Kunapuli
Multi-Agent Inverse Reinforcement Learning. ICMLA 2010: 395-400 - [c78]Houssam Nassif, David Page, Mehmet Ayvaci, Jude W. Shavlik, Elizabeth S. Burnside:
Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming. IHI 2010: 76-82 - [c77]Trevor Walker, Ciaran O'Reilly, Gautam Kunapuli, Sriraam Natarajan, Richard Maclin, David Page, Jude W. Shavlik:
Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge. ILP 2010: 253-268 - [c76]Gautam Kunapuli, Kristin P. Bennett, Amina Shabbeer, Richard Maclin, Jude W. Shavlik:
Online Knowledge-Based Support Vector Machines. ECML/PKDD (2) 2010: 145-161 - [c75]Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik:
Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. ECML/PKDD (2) 2010: 434-450 - [p4]Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin
Transfer Learning via Advice Taking. Advances in Machine Learning I 2010: 147-170
2000 – 2009
- 2009
- [j30]Frank DiMaio, Ameet Soni
Spherical-harmonic decomposition for molecular recognition in electron-density maps. Int. J. Data Min. Bioinform. 3(2): 205-227 (2009) - [j29]Bee-Chung Chen, Raghu Ramakrishnan, Jude W. Shavlik, Pradeep Tamma:
Bellwether analysis: Searching for cost-effective query-defined predictors in large databases. ACM Trans. Knowl. Discov. Data 3(1): 5:1-5:49 (2009) - [c74]Houssam Nassif, Ryan W. Woods, Elizabeth S. Burnside
Information Extraction for Clinical Data Mining: A Mammography Case Study. ICDM Workshops 2009: 37-42 - [c73]Sriraam Natarajan, Prasad Tadepalli
Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule. ICMLA 2009: 141-146 - [c72]Jude W. Shavlik, Sriraam Natarajan:
Speeding Up Inference in Markov Logic Networks by Preprocessing to Reduce the Size of the Resulting Grounded Network. IJCAI 2009: 1951-1956 - [c71]Louis Oliphant, Elizabeth S. Burnside, Jude W. Shavlik:
Boosting First-Order Clauses for Large, Skewed Data Sets. ILP 2009: 166-177 - [c70]Lisa Torrey, Jude W. Shavlik:
Policy Transfer via Markov Logic Networks. ILP 2009: 234-248 - 2008
- [j28]Hendrik Blockeel
Guest editors' introduction: special issue on inductive logic programming (ILP-2007). Mach. Learn. 73(1): 1-2 (2008) - [c69]Lisa Torrey, Trevor Walker, Richard Maclin, Jude W. Shavlik:
Advice Taking and Transfer Learning: Naturally Inspired Extensions to Reinforcement Learning. AAAI Fall Symposium: Naturally-Inspired Artificial Intelligence 2008: 103-110 - [p3]Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin
Rule Extraction for Transfer Learning. Rule Extraction from Support Vector Machines 2008: 67-82 - [e3]Hendrik Blockeel
Inductive Logic Programming, 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers. Lecture Notes in Computer Science 4894, Springer 2008, ISBN 978-3-540-78468-5 [contents] - 2007
- [j27]Frank DiMaio, Dmitry A. Kondrashov
Creating protein models from electron-density maps using particle-filtering methods. Bioinform. 23(21): 2851-2858 (2007) - [c68]Richard Maclin, Edward W. Wild, Jude W. Shavlik, Lisa Torrey, Trevor Walker:
Refining Rules Incorporated into Knowledge-Based Support Vector Learners Via Successive Linear Programming. AAAI 2007: 584-589 - [c67]Frank DiMaio, Ameet Soni, George N. Phillips, Jude W. Shavlik:
Improved Methods for Template-Matching in Electron-Density Maps Using Spherical Harmonics. BIBM 2007: 258-265 - [c66]Mark Goadrich, Jude W. Shavlik:
Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates. ILP 2007: 122-131 - [c65]Louis Oliphant, Jude W. Shavlik:
Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming. ILP 2007: 191-199 - [c64]Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin
Relational Macros for Transfer in Reinforcement Learning. ILP 2007: 254-268 - [c63]Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richard Maclin
Building Relational World Models for Reinforcement Learning. ILP 2007: 280-291 - 2006
- [j26]Mark Goadrich, Louis Oliphant, Jude W. Shavlik:
Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves. Mach. Learn. 64(1-3): 231-261 (2006) - [c62]Richard Maclin, Jude W. Shavlik, Trevor Walker, Lisa Torrey:
A Simple and Effective Method for Incorporating Advice into Kernel Methods. AAAI 2006: 427-432 - [c61]Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin:
Skill Acquisition Via Transfer Learning and Advice Taking. ECML 2006: 425-436 - [c60]Frank DiMaio, Jude W. Shavlik:
Belief Propagation in Large, Highly Connected Graphs for 3D Part-Based Object Recognition. ICDM 2006: 845-850 - [c59]Frank DiMaio, Jude W. Shavlik, George N. Phillips:
A probabilistic approach to protein backbone tracing in electron density maps. ISMB (Supplement of Bioinformatics) 2006: 81-89 - [c58]Bee-Chung Chen, Raghu Ramakrishnan, Jude W. Shavlik, Pradeep Tamma:
Bellwether Analysis: Predicting Global Aggregates from Local Regions. VLDB 2006: 655-666 - 2005
- [c57]Richard Maclin, Jude W. Shavlik, Lisa Torrey, Trevor Walker, Edward W. Wild:
Giving Advice about Preferred Actions to Reinforcement Learners Via Knowledge-Based Kernel Regression. AAAI 2005: 819-824 - [c56]Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richard Maclin:
Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another. ECML 2005: 412-424 - [c55]Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Raghu Ramakrishnan, Vítor Santos Costa, Jude W. Shavlik:
View Learning for Statistical Relational Learning: With an Application to Mammography. IJCAI 2005: 677-683 - [c54]Héctor Corrada Bravo, David Page, Raghu Ramakrishnan, Jude W. Shavlik, Vítor Santos Costa
A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment. ILP 2005: 69-86 - [c53]Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richard Maclin
Knowledge transfer via advice taking. K-CAP 2005: 217-218 - 2004
- [j25]Michael Molla, Michael Waddell, David Page, Jude W. Shavlik:
Using Machine Learning to Design and Interpret Gene-Expression Microarrays. AI Mag. 25(1): 23-44 (2004) - [j24]Olvi L. Mangasarian, Jude W. Shavlik, Edward W. Wild:
Knowledge-Based Kernel Approximation. J. Mach. Learn. Res. 5: 1127-1141 (2004) - [c52]Michael Molla, Jude W. Shavlik, Thomas Albert, Todd Richmond, Steven Smith:
A Self-Tuning Method for One-Chip SNP Identification. CSB 2004: 69-79 - [c51]Jude W. Shavlik:
Scaling Up ILP: Experiences with Extracting Relations from Biomedical Text. ILP 2004: 7 - [c50]Frank DiMaio, Jude W. Shavlik:
Learning an Approximation to Inductive Logic Programming Clause Evaluation. ILP 2004: 80-97 - [c49]Mark Goadrich, Louis Oliphant, Jude W. Shavlik:
Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction. ILP 2004: 98-115 - [c48]Jude W. Shavlik, Mark Shavlik:
Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage. KDD 2004: 276-285 - [c47]Frank DiMaio, Jude W. Shavlik, George N. Phillips:
Pictorial Structures for Molecular Modeling: Interpreting Density Maps. NIPS 2004: 369-376 - 2003
- [j23]Joseph Bockhorst, Mark W. Craven, David Page, Jude W. Shavlik, Jeremy D. Glasner:
A Bayesian Network Approach to Operon Prediction. Bioinform. 19(10): 1227-1235 (2003) - [j22]Tina Eliassi-Rad, Jude W. Shavlik:
A System for Building Intelligent Agents that Learn to Retrieve and Extract Information. User Model. User Adapt. Interact. 13(1-2): 35-88 (2003) - [c46]Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik:
Knowledge-Based Nonlinear Kernel Classifiers. COLT 2003: 102-113 - [c45]Inês de Castro Dutra
Toward Automatic Management of Embarrassingly Parallel Applications. Euro-Par 2003: 509-516 - [c44]Fernanda Araújo Baião, Marta Mattoso, Jude W. Shavlik, Gerson Zaverucha:
Applying Theory Revision to the Design of Distributed Databases. ILP 2003: 57-74 - [p2]Tina Eliassi-Rad, Jude W. Shavlik:
Intelligent Web Agents that Learn to Retrieve and Extract Information. Intelligent Exploration of the Web 2003: 255-274 - 2002
- [j21]Yolanda Gil, Mark A. Musen, Jude W. Shavlik:
Report on the First International Conference on Knowledge Capture (K-CAP). AI Mag. 23(4): 107-108 (2002) - [j20]Michael Molla, Peter Andreae, Jeremy D. Glasner, Frederick R. Blattner, Jude W. Shavlik:
Interpreting microarray expression data using text annotating the genes. Inf. Sci. 146(1-4): 75-88 (2002) - [c43]Inês de Castro Dutra
An Empirical Evaluation of Bagging in Inductive Logic Programming. ILP 2002: 48-65 - [c42]J. B. Tobler, Michael Molla, Emile F. Nuwaysir, R. D. Green, Jude W. Shavlik:
Evaluating machine learning approaches for aiding probe selection for gene-expression arrays. ISMB 2002: 164-171 - [c41]Michael Molla, Peter Andreae, Jeremy D. Glasner, Frederick R. Blattner, Jude W. Shavlik:
Interpreting Microarray Expression Data Using Text Annotating the Genes. JCIS 2002: 1224-1230 - [c40]Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik:
Knowledge-Based Support Vector Machine Classifiers. NIPS 2002: 521-528 - 2001
- [c39]Tina Eliassi-Rad, Jude W. Shavlik:
A Theory-Refinement Approach to Information Extraction. ICML 2001: 130-137 - 2000
- [c38]Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner:
Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes. ICML 2000: 199-206 - [c37]Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner:
A Probabilistic Learning Approach to Whole-Genome Operon Prediction. ISMB 2000: 116-127 - [c36]Jeremy Goecks, Jude W. Shavlik:
Learning users' interests by unobtrusively observing their normal behavior. IUI 2000: 129-132
1990 – 1999
- 1999
- [j19]Carolyn F. Allex, Jude W. Shavlik, Frederick R. Blattner:
Neural network input representations that produce accurate consensus sequences from DNA fragment assemblies. Bioinform. 15(9): 723-728 (1999) - [c35]Jude W. Shavlik, Lawrence Birnbaum, William R. Swartout
Bridging Science and Applications (Panel). IUI 1999: 45-46 - [c34]Jude W. Shavlik, Susan Calcari, Tina Eliassi-Rad, Jack Solock:
An Instructable, Adaptive Interface for Discovering and Monitoring Information on the World-Wide Web. IUI 1999: 157-160 - 1998
- [p1]Richard Maclin, Jude W. Shavlik:
Creating Advice-Taking Reinforcement Learners. Learning to Learn 1998: 311-347 - [e2]Jude W. Shavlik:
Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, July 24-27, 1998. Morgan Kaufmann 1998, ISBN 1-55860-556-8 [contents] - 1997
- [j18]Mark W. Craven, Jude W. Shavlik:
Using neural networks for data mining. Future Gener. Comput. Syst. 13(2-3): 211-229 (1997) - [j17]Mark W. Craven, Jude W. Shavlik:
Understanding Time-Series Networks: A Case Study in Rule Extraction. Int. J. Neural Syst. 8(4): 373-384 (1997) - [j16]David W. Opitz, Jude W. Shavlik:
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies. J. Artif. Intell. Res. 6: 177-209 (1997) - [c33]David W. Opitz, Mark W. Craven, Jude W. Shavlik:
Using neural networks to automatically refine expert system knowledge bases: experiments in the NYNEX MAX domain. ICNN 1997: 16-20 - [c32]Carolyn F. Allex, Schuyler F. Baldwin, Jude W. Shavlik, Frederick R. Blattner:
Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations. ISMB 1997: 3-14 - [i1]David W. Opitz, Jude W. Shavlik:
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies. CoRR cs.AI/9705102 (1997) - 1996
- [j15]David W. Opitz, Jude W. Shavlik:
Actively Searching for an Effective Neural Network Ensemble. Connect. Sci. 8(3): 337-354 (1996) - [j14]Richard Maclin, Jude W. Shavlik:
Creating Advice-Taking Reinforcement Learners. Mach. Learn. 22(1-3): 251-281 (1996) - [c31]Carolyn F. Allex, Schuyler F. Baldwin, Jude W. Shavlik, Frederick R. Blattner:
Improving the Quality of Automatic DNA Sequence Assembly Using Fluorescent Trace-Data Classifications. ISMB 1996: 3-14 - [c30]Kevin J. Cherkauer, Jude W. Shavlik:
Growing Simpler Decision Trees to Facilitate Knowledge Discovery. KDD 1996: 315-318 - 1995
- [j13]David W. Opitz, Jude W. Shavlik:
Dynamically adding symbolically meaningful nodes to knowledge-based neural networks. Knowl. Based Syst. 8(6): 301-311 (1995) - [j12]Jude W. Shavlik, Lawrence Hunter
Introduction. Mach. Learn. 21(1-2): 5-9 (1995) - [c29]Richard Maclin, Jude W. Shavlik:
Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks. IJCAI 1995: 524-531 - [c28]Mark W. Craven, Jude W. Shavlik:
Extracting Tree-Structured Representations of Trained Networks. NIPS 1995: 24-30 - [c27]Kevin J. Cherkauer, Jude W. Shavlik:
Rapid Quality Estimation of Neural Network Input Representations. NIPS 1995: 45-51 - [c26]David W. Opitz, Jude W. Shavlik:
Generating Accurate and Diverse Members of a Neural-Network Ensemble. NIPS 1995: 535-541 - 1994
- [j11]Geoffrey G. Towell, Jude W. Shavlik:
Knowledge-Based Artificial Neural Networks. Artif. Intell. 70(1-2): 119-165 (1994) - [j10]David B. Searls, Jude W. Shavlik, Lawrence Hunter:
The First International Conference on Intelligent Systems for Molecular Biology. AI Mag. 15(1): 12-13 (1994) - [j9]Mark W. Craven, Jude W. Shavlik:
Machine Learning Approaches to Gene Recognition. IEEE Expert 9(2): 2-10 (1994) - [j8]Jude W. Shavlik:
Combining Symbolic and Neural Learning. Mach. Learn. 14(1): 321-331 (1994) - [c25]Richard Maclin, Jude W. Shavlik:
Incorporating Advice into Agents that Learn from Reinforcements. AAAI 1994: 694-699 - [c24]Mark W. Craven, Jude W. Shavlik:
Using Sampling and Queries to Extract Rules from Trained Neural Networks. ICML 1994: 37-45 - [c23]David W. Opitz, Jude W. Shavlik:
Using Genetic Search to Refine Knowledge-based Neural Networks. ICML 1994: 208-216 - 1993
- [j7]Richard Maclin, Jude W. Shavlik:
Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding. Mach. Learn. 11: 195-215 (1993) - [j6]Geoffrey G. Towell, Jude W. Shavlik:
Extracting Refined Rules from Knowledge-Based Neural Networks. Mach. Learn. 13: 71-101 (1993) - [c22]Mark W. Craven, Jude W. Shavlik:
Learning Symbolic Rules Using Artificial Neural Networks. ICML 1993: 73-80 - [c21]Mark W. Craven, Jude W. Shavlik:
Learning to Represent Codons: A Challenge Problem for Constructive Induction. IJCAI 1993: 1319-1324 - [c20]David W. Opitz, Jude W. Shavlik:
Heuristically Expanding Knowledge-Based Neural Networks. IJCAI 1993: 1360-1365 - [c19]Kevin J. Cherkauer, Jude W. Shavlik:
Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools. ISMB 1993: 74-82 - [e1]Lawrence Hunter, David B. Searls, Jude W. Shavlik:
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology, Bethesda, MD, USA, July 1993. AAAI 1993, ISBN 0-929280-47-4 [contents] - 1992
- [j5]Mark W. Craven, Jude W. Shavlik:
Visualizing Learning and Computation in Artificial Neural Networks. Int. J. Artif. Intell. Tools 1(3): 399-426 (1992) - [j4]Gary M. Scott, Jude W. Shavlik, W. Harmon Ray:
Refining PID Controllers Using Neural Networks. Neural Comput. 4(5): 746-757 (1992) - [c18]Richard Maclin, Jude W. Shavlik:
Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding. AAAI 1992: 165-170 - [c17]Geoffrey G. Towell, Jude W. Shavlik:
Using Symbolic Learning to Improve Knowledge-Based Neural Networks. AAAI 1992: 177-182 - 1991
- [j3]Jude W. Shavlik, Raymond J. Mooney, Geoffrey G. Towell:
Symbolic and Neural Learning Algorithms: An Experimental Comparison. Mach. Learn. 6: 111-143 (1991) - [c16]Geoffrey G. Towell, Mark W. Craven, Jude W. Shavlik:
Constructive Induction in Knowledge-Based Neural Networks. ML 1991: 213-217 - [c15]Richard Maclin
Refining Domain Theories Expressed as Finite-State Automata. ML 1991: 524-528 - [c14]Gary M. Scott, Jude W. Shavlik, W. Harmon Ray:
Refined PID Controllers Using Neural Networks. NIPS 1991: 555-562 - [c13]Geoffrey G. Towell, Jude W. Shavlik:
Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules. NIPS 1991: 977-984 - 1990
- [j2]Jude W. Shavlik, Gerald DeJong:
Learning in Mathematically-Based Domains: Understanding and Generalizing Obstacle Cancellations. Artif. Intell. 45(1-2): 1-45 (1990) - [j1]Jude W. Shavlik:
Acquiring Recursive and Iterative Concepts with Explanation-Based Learning. Mach. Learn. 5: 39-40 (1990) - [c12]Geoffrey G. Towell, Jude W. Shavlik, Michiel O. Noordewier:
Refinement ofApproximate Domain Theories by Knowledge-Based Neural Networks. AAAI 1990: 861-866 - [c11]Michiel O. Noordewier, Geoffrey G. Towell, Jude W. Shavlik:
Training Knowledge-Based Neural Networks to Recognize Genes. NIPS 1990: 530-536
1980 – 1989
- 1989
- [c10]Jude W. Shavlik, Geoffrey G. Towell:
Combining Explanation-Based Learning and Artificial Neural Networks. ML 1989: 90-93 - [c9]Douglas H. Fisher, Kathleen B. McKusick, Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell:
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems. ML 1989: 169-173 - [c8]Jude W. Shavlik:
An Empirical Analysis of EBL Approaches for Learning Plan Schemata. ML 1989: 183-187 - [c7]Richard Maclin, Jude W. Shavlik:
Enriching Vocabularies by Generalizing Explanation Structures. ML 1989: 444-446 - [c6]Jude W. Shavlik:
Acquiring Recursive Concepts with Explanation-Based Learning. IJCAI 1989: 688-693 - [c5]Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell, Alan Gove:
An Experimental Comparison of Symbolic and Connectionist Learning Algorithms. IJCAI 1989: 775-780 - 1988
- [b1]Jude W. Shavlik:
Generalizing the Structure of Explanations in Explanation-Based Learning. University of Illinois Urbana-Champaign, USA, 1988 - 1987
- [c4]Jude W. Shavlik, Gerald DeJong:
BAGGER: An EBL System that Extends and Generalizes Explanations. AAAI 1987: 516-520 - [c3]Jude W. Shavlik, Gerald DeJong:
An Explanation-based Approach to Generalizing Number. IJCAI 1987: 236-238 - 1986
- [c2]Jude W. Shavlik, Gerald DeJong:
Computer understanding and generalization of symbolic mathematical calculations: a case study in physics problem solving. SYMSAC 1986: 148-153 - 1985
- [c1]Jude W. Shavlik:
Learning about Momentum Conservation. IJCAI 1985: 667-669
Coauthor Index
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