Jude W. Shavlik

According to our database, Jude W. Shavlik authored at least 150 papers between 1985 and 2021.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2021

A Recap of Early Work on Theory and Knowledge Refinement.

Raymond J. Mooney, Jude W. Shavlik

Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021), 2021

2016

Learning Relational Dependency Networks for Relation Extraction.

Ameet Soni, Dileep Viswanathan, Jude W. Shavlik, Sriraam Natarajan

Proceedings of the Inductive Logic Programming - 26th International Conference, 2016

2015

Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases.

Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik

Mach. Learn., 2015

TAC KBP 2015 : English Slot Filling Track Relational Learning with Expert Advice.

Dileep Viswanathan, Anurag Wazalwar, Sriraam Natarajan, Ameet Soni, Jude W. Shavlik

Proceedings of the 2015 Text Analysis Conference, 2015

Anomaly Detection in Text: The Value of Domain Knowledge.

Raksha Kumaraswamy, Anurag Wazalwar, Tushar Khot, Jude W. Shavlik, Sriraam Natarajan

Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, 2015

2014

Boosted Statistical Relational Learners - From Benchmarks to Data-Driven Medicine

Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude W. Shavlik

Springer Briefs in Computer Science, Springer, ISBN: 978-3-319-13644-8, 2014

Corleone: hands-off crowdsourcing for entity matching.

Chaitanya Gokhale, Sanjib Das, AnHai Doan, Jeffrey F. Naughton, Narasimhan Rampalli, Jude W. Shavlik, Xiaojin Zhu

Proceedings of the International Conference on Management of Data, 2014

Detecting Semantic Uncertainty by Learning Hedge Cues in Sentences Using an HMM.

Xiujun Li, Wei Gao, Jude W. Shavlik

Proceedings of Workshop on Semantic Matching in Information Retrieval co-located with the 37th international ACM SIGIR conference on research and development in information retrieval, 2014

Support Vector Machines for Differential Prediction.

Finn Kuusisto, Vítor Santos Costa, Houssam Nassif, Elizabeth S. Burnside, David Page, Jude W. Shavlik

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge.

Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Ré, Jude W. Shavlik

Proceedings of the Inductive Logic Programming - 24th International Conference, 2014

Classification from One Class of Examples for Relational Domains.

Tushar Khot, Sriraam Natarajan, Jude W. Shavlik

Proceedings of the Statistical Relational Artificial Intelligence, 2014

Relational One-Class Classification: A Non-Parametric Approach.

Tushar Khot, Sriraam Natarajan, Jude W. Shavlik

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

Bootstrapping Knowledge Base Acceleration.

Tushar Khot, Ce Zhang, Jude W. Shavlik, Sriraam Natarajan, Christopher Ré

Proceedings of The Twenty-Second Text REtrieval Conference, 2013

Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling.

Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, David Page, Jude W. Shavlik, Vítor Santos Costa

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Uplift Modeling with ROC: An SRL Case Study.

Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, Jude W. Shavlik

Proceedings of the Late Breaking Papers of the 23rd International Conference on Inductive Logic Programming, Rio de Janeiro, Brazil, August 28th - to, 2013

Guiding Autonomous Agents to Better Behaviors through Human Advice.

Gautam Kunapuli, Phillip Odom, Jude W. Shavlik, Sriraam Natarajan

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Using machine learning to identify benign cases with non-definitive biopsy.

Finn Kuusisto, Inês de Castro Dutra, Houssam Nassif, Yirong Wu, Molly E. Klein, Heather B. Neuman, Jude W. Shavlik, Elizabeth S. Burnside

Proceedings of the IEEE 15th International Conference on e-Health Networking, 2013

Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms.

Jie Liu, David Page, Houssam Nassif, Jude W. Shavlik, Peggy L. Peissig, Catherine A. McCarty, Adedayo A. Onitilo, Elizabeth S. Burnside

Proceedings of the AMIA 2013, 2013

Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text.

Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Ré, Jude W. Shavlik

Proceedings of the Statistical Relational Artificial Intelligence, 2013

2012

Gradient-based boosting for statistical relational learning: The relational dependency network case.

Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann, Jude W. Shavlik

Mach. Learn., 2012

Probabilistic Ensembles for Improved Inference in protein-Structure Determination.

Ameet Soni, Jude W. Shavlik

J. Bioinform. Comput. Biol., 2012

Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference.

Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik

Int. J. Semantic Web Inf. Syst., 2012

DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference.

Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik

Proceedings of the Second International Workshop on Searching and Integrating New Web Data Sources, 2012

Learning Relational Structure for Temporal Relation Extraction.

Tushar Khot, Siddharth Srivastava, Sriraam Natarajan, Jude W. Shavlik

Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Mirror Descent for Metric Learning: A Unified Approach.

Gautam Kunapuli, Jude W. Shavlik

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Twenty-Five Years of Combining Symbolic and Numeric Learning.

Jude W. Shavlik

Proceedings of the Neural-Symbolic Learning and Reasoning (NeSy 2012), 2012

Scaling Inference for Markov Logic via Dual Decomposition.

Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik

Proceedings of the 12th IEEE International Conference on Data Mining, 2012

Big Data versus the Crowd: Looking for Relationships in All the Right Places.

Ce Zhang, Feng Niu, Christopher Ré, Jude W. Shavlik

Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, July 8-14, 2012, Jeju Island, Korea, 2012

Invited Talks.

Timothy W. Clark, William W. Cohen, Lawrence Hunter, Chris J. Lintott, Jude W. Shavlik

Proceedings of the Discovery Informatics: The Role of AI Research in Innovating Scientific Processes, 2012

2011

Predictive Models in Personalized Medicine: Neural Information Processing Systems (NIPS), 2010 workshop report.

Faisal Farooq, Balaji Krishnapuram, Rómer Rosales, Shipeng Yu, Jude W. Shavlik, Raju Kucherlapati

SIGHIT Rec., 2011

Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS.

Feng Niu, Christopher Ré, AnHai Doan, Jude W. Shavlik

Proc. VLDB Endow., 2011

Felix: Scaling Inference for Markov Logic with an Operator-based Approach

Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik

CoRR, 2011

Advice Refinement in Knowledge-Based SVMs.

Gautam Kunapuli, Richard Maclin, Jude W. Shavlik

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Integrating knowledge capture and supervised learning through a human-computer interface.

Trevor Walker, Gautam Kunapuli, Noah Larsen, David Page, Jude W. Shavlik

Proceedings of the 6th International Conference on Knowledge Capture (K-CAP 2011), 2011

Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach.

Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik

Proceedings of the IJCAI 2011, 2011

Learning Markov Logic Networks via Functional Gradient Boosting.

Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

2010

Transfer Learning via Advice Taking.

Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin

Proceedings of the Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S. Michalski, 2010

Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer.

Ryan W. Woods, Louis Oliphant, Kazuhiko Shinki, David Page, Jude W. Shavlik, Elizabeth S. Burnside

J. Digit. Imaging, 2010

Online Knowledge-Based Support Vector Machines.

Gautam Kunapuli, Kristin P. Bennett, Amina Shabbeer, Richard Maclin, Jude W. Shavlik

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge.

Trevor Walker, Ciaran O'Reilly, Gautam Kunapuli, Sriraam Natarajan, Richard Maclin, David Page, Jude W. Shavlik

Proceedings of the Inductive Logic Programming - 20th International Conference, 2010

Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming.

Houssam Nassif, David Page, Mehmet Ayvaci, Jude W. Shavlik, Elizabeth S. Burnside

Proceedings of the ACM International Health Informatics Symposium, 2010

Multi-Agent Inverse Reinforcement Learning.

Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik

Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010

Guiding belief propagation using domain knowledge for protein-structure determination.

Ameet Soni, Craig A. Bingman, Jude W. Shavlik

Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology, 2010

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models.

Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik

Proceedings of the Statistical Relational Artificial Intelligence, 2010

2009

Bellwether analysis: Searching for cost-effective query-defined predictors in large databases.

Bee-Chung Chen, Raghu Ramakrishnan, Jude W. Shavlik, Pradeep Tamma

ACM Trans. Knowl. Discov. Data, 2009

Spherical-harmonic decomposition for molecular recognition in electron-density maps.

Frank DiMaio, Ameet Soni, George N. Phillips, Jude W. Shavlik

Int. J. Data Min. Bioinform., 2009

Policy Transfer via Markov Logic Networks.

Lisa Torrey, Jude W. Shavlik

Proceedings of the Inductive Logic Programming, 19th International Conference, 2009

Boosting First-Order Clauses for Large, Skewed Data Sets.

Louis Oliphant, Elizabeth S. Burnside, Jude W. Shavlik

Proceedings of the Inductive Logic Programming, 19th International Conference, 2009

Speeding Up Inference in Markov Logic Networks by Preprocessing to Reduce the Size of the Resulting Grounded Network.

Jude W. Shavlik, Sriraam Natarajan

Proceedings of the IJCAI 2009, 2009

Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule.

Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapuli, Jude W. Shavlik

Proceedings of the International Conference on Machine Learning and Applications, 2009

Information Extraction for Clinical Data Mining: A Mammography Case Study.

Houssam Nassif, Ryan W. Woods, Elizabeth S. Burnside, Mehmet Ayvaci, Jude W. Shavlik, David Page

Proceedings of the ICDM Workshops 2009, 2009

2008

Rule Extraction for Transfer Learning.

Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin

Proceedings of the Rule Extraction from Support Vector Machines, 2008

Guest editors' introduction: special issue on inductive logic programming (ILP-2007).

Hendrik Blockeel, Jude W. Shavlik, Prasad Tadepalli

Mach. Learn., 2008

Advice Taking and Transfer Learning: Naturally Inspired Extensions to Reinforcement Learning.

Lisa Torrey, Trevor Walker, Richard Maclin, Jude W. Shavlik

Proceedings of the Naturally-Inspired Artificial Intelligence, 2008

2007

Creating protein models from electron-density maps using particle-filtering methods.

Frank DiMaio, Dmitry A. Kondrashov, Eduard Bitto, Ameet Soni, Craig A. Bingman, George N. Phillips Jr., Jude W. Shavlik

Bioinform., 2007

Building Relational World Models for Reinforcement Learning.

Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richard Maclin

Proceedings of the Inductive Logic Programming, 17th International Conference, 2007

Relational Macros for Transfer in Reinforcement Learning.

Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin

Proceedings of the Inductive Logic Programming, 17th International Conference, 2007

Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming.

Louis Oliphant, Jude W. Shavlik

Proceedings of the Inductive Logic Programming, 17th International Conference, 2007

Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates.

Mark Goadrich, Jude W. Shavlik

Proceedings of the Inductive Logic Programming, 17th International Conference, 2007

Improved Methods for Template-Matching in Electron-Density Maps Using Spherical Harmonics.

Frank DiMaio, Ameet Soni, George N. Phillips, Jude W. Shavlik

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2007

Refining Rules Incorporated into Knowledge-Based Support Vector Learners Via Successive Linear Programming.

Richard Maclin, Edward W. Wild, Jude W. Shavlik, Lisa Torrey, Trevor Walker

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006

Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves.

Mark Goadrich, Louis Oliphant, Jude W. Shavlik

Mach. Learn., 2006

Bellwether Analysis: Predicting Global Aggregates from Local Regions.

Bee-Chung Chen, Raghu Ramakrishnan, Jude W. Shavlik, Pradeep Tamma

Proceedings of the 32nd International Conference on Very Large Data Bases, 2006

A probabilistic approach to protein backbone tracing in electron density maps.

Frank DiMaio, Jude W. Shavlik, George N. Phillips

Proceedings of the Proceedings 14th International Conference on Intelligent Systems for Molecular Biology 2006, 2006

Belief Propagation in Large, Highly Connected Graphs for 3D Part-Based Object Recognition.

Frank DiMaio, Jude W. Shavlik

Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

Skill Acquisition Via Transfer Learning and Advice Taking.

Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin

Proceedings of the Machine Learning: ECML 2006, 2006

A Simple and Effective Method for Incorporating Advice into Kernel Methods.

Richard Maclin, Jude W. Shavlik, Trevor Walker, Lisa Torrey

Proceedings of the Proceedings, 2006

2005

Knowledge transfer via advice taking.

Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richard Maclin

Proceedings of the 3rd International Conference on Knowledge Capture (K-CAP 2005), 2005

A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment.

Héctor Corrada Bravo, David Page, Raghu Ramakrishnan, Jude W. Shavlik, Vítor Santos Costa

Proceedings of the Inductive Logic Programming, 15th International Conference, 2005

View Learning for Statistical Relational Learning: With an Application to Mammography.

Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Raghu Ramakrishnan, Vítor Santos Costa, Jude W. Shavlik

Proceedings of the IJCAI-05, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, UK, July 30, 2005

Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another.

Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richard Maclin

Proceedings of the Machine Learning: ECML 2005, 2005

Giving Advice about Preferred Actions to Reinforcement Learners Via Knowledge-Based Kernel Regression.

Richard Maclin, Jude W. Shavlik, Lisa Torrey, Trevor Walker, Edward W. Wild

Proceedings of the Proceedings, 2005

2004

Knowledge-Based Kernel Approximation.

Olvi L. Mangasarian, Jude W. Shavlik, Edward W. Wild

J. Mach. Learn. Res., 2004

Using Machine Learning to Design and Interpret Gene-Expression Microarrays.

Michael Molla, Michael Waddell, David Page, Jude W. Shavlik

AI Mag., 2004

Pictorial Structures for Molecular Modeling: Interpreting Density Maps.

Frank DiMaio, Jude W. Shavlik, George N. Phillips

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage.

Jude W. Shavlik, Mark Shavlik

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

Scaling Up ILP: Experiences with Extracting Relations from Biomedical Text.

Jude W. Shavlik

Proceedings of the Inductive Logic Programming, 14th International Conference, 2004

Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction.

Mark Goadrich, Louis Oliphant, Jude W. Shavlik

Proceedings of the Inductive Logic Programming, 14th International Conference, 2004

Learning an Approximation to Inductive Logic Programming Clause Evaluation.

Frank DiMaio, Jude W. Shavlik

Proceedings of the Inductive Logic Programming, 14th International Conference, 2004

A Self-Tuning Method for One-Chip SNP Identification.

Michael Molla, Jude W. Shavlik, Thomas Albert, Todd Richmond, Steven Smith

Proceedings of the 3rd International IEEE Computer Society Computational Systems Bioinformatics Conference, 2004

2003

Intelligent Web Agents that Learn to Retrieve and Extract Information.

Tina Eliassi-Rad, Jude W. Shavlik

Proceedings of the Intelligent Exploration of the Web, 2003

A System for Building Intelligent Agents that Learn to Retrieve and Extract Information.

Tina Eliassi-Rad, Jude W. Shavlik

User Model. User Adapt. Interact., 2003

A Bayesian Network Approach to Operon Prediction.

Joseph Bockhorst, Mark W. Craven, David Page, Jude W. Shavlik, Jeremy D. Glasner

Bioinform., 2003

Applying Theory Revision to the Design of Distributed Databases.

Fernanda Araújo Baião, Marta Mattoso, Jude W. Shavlik, Gerson Zaverucha

Proceedings of the Inductive Logic Programming: 13th International Conference, 2003

Toward Automatic Management of Embarrassingly Parallel Applications.

Inês de Castro Dutra, David Page, Vítor Santos Costa, Jude W. Shavlik, Michael Waddell

Proceedings of the Euro-Par 2003. Parallel Processing, 2003

Knowledge-Based Nonlinear Kernel Classifiers.

Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik

Proceedings of the Computational Learning Theory and Kernel Machines, 2003

2002

Interpreting microarray expression data using text annotating the genes.

Michael Molla, Peter Andreae, Jeremy D. Glasner, Frederick R. Blattner, Jude W. Shavlik

Inf. Sci., 2002

Report on the First International Conference on Knowledge Capture (K-CAP).

Yolanda Gil, Mark A. Musen, Jude W. Shavlik

AI Mag., 2002

Knowledge-Based Support Vector Machine Classifiers.

Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Evaluating machine learning approaches for aiding probe selection for gene-expression arrays.

J. B. Tobler, Michael Molla, Emile F. Nuwaysir, R. D. Green, Jude W. Shavlik

Proceedings of the Tenth International Conference on Intelligent Systems for Molecular Biology, 2002

An Empirical Evaluation of Bagging in Inductive Logic Programming.

Inês de Castro Dutra, David Page, Vítor Santos Costa, Jude W. Shavlik

Proceedings of the Inductive Logic Programming, 12th International Conference, 2002

2001

A Theory-Refinement Approach to Information Extraction.

Tina Eliassi-Rad, Jude W. Shavlik

Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001

2000

Learning users' interests by unobtrusively observing their normal behavior.

Jeremy Goecks, Jude W. Shavlik

Proceedings of the 5th International Conference on Intelligent User Interfaces, 2000

A Probabilistic Learning Approach to Whole-Genome Operon Prediction.

Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 2000

Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes.

Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

1999

Neural network input representations that produce accurate consensus sequences from DNA fragment assemblies.

Carolyn F. Allex, Jude W. Shavlik, Frederick R. Blattner

Bioinform., 1999

An Instructable, Adaptive Interface for Discovering and Monitoring Information on the World-Wide Web.

Jude W. Shavlik, Susan Calcari, Tina Eliassi-Rad, Jack Solock

Proceedings of the 4th International Conference on Intelligent User Interfaces, 1999

Bridging Science and Applications (Panel).

Jude W. Shavlik, Lawrence Birnbaum, William R. Swartout, Eric Horvitz, Barbara Hayes-Roth

Proceedings of the 4th International Conference on Intelligent User Interfaces, 1999

1998

Creating Advice-Taking Reinforcement Learners.

Richard Maclin, Jude W. Shavlik

Proceedings of the Learning to Learn., 1998

1997

Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies.

David W. Opitz, Jude W. Shavlik

J. Artif. Intell. Res., 1997

Understanding Time-Series Networks: A Case Study in Rule Extraction.

Mark W. Craven, Jude W. Shavlik

Int. J. Neural Syst., 1997

Using neural networks for data mining.

Mark W. Craven, Jude W. Shavlik

Future Gener. Comput. Syst., 1997

Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations.

Carolyn F. Allex, Schuyler F. Baldwin, Jude W. Shavlik, Frederick R. Blattner

Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology, 1997

Using neural networks to automatically refine expert system knowledge bases: experiments in the NYNEX MAX domain.

David W. Opitz, Mark W. Craven, Jude W. Shavlik

Proceedings of International Conference on Neural Networks (ICNN'97), 1997

1996

Creating Advice-Taking Reinforcement Learners.

Richard Maclin, Jude W. Shavlik

Mach. Learn., 1996

Actively Searching for an Effective Neural Network Ensemble.

David W. Opitz, Jude W. Shavlik

Connect. Sci., 1996

Growing Simpler Decision Trees to Facilitate Knowledge Discovery.

Kevin J. Cherkauer, Jude W. Shavlik

Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 1996

Improving the Quality of Automatic DNA Sequence Assembly Using Fluorescent Trace-Data Classifications.

Carolyn F. Allex, Schuyler F. Baldwin, Jude W. Shavlik, Frederick R. Blattner

Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology, 1996

1995

Introduction.

Jude W. Shavlik, Lawrence Hunter, David B. Searls

Mach. Learn., 1995

Dynamically adding symbolically meaningful nodes to knowledge-based neural networks.

David W. Opitz, Jude W. Shavlik

Knowl. Based Syst., 1995

Generating Accurate and Diverse Members of a Neural-Network Ensemble.

David W. Opitz, Jude W. Shavlik

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Extracting Tree-Structured Representations of Trained Networks.

Mark W. Craven, Jude W. Shavlik

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Rapid Quality Estimation of Neural Network Input Representations.

Kevin J. Cherkauer, Jude W. Shavlik

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks.

Richard Maclin, Jude W. Shavlik

Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1995

1994

Combining Symbolic and Neural Learning.

Jude W. Shavlik

Mach. Learn., 1994

Machine Learning Approaches to Gene Recognition.

Mark W. Craven, Jude W. Shavlik

IEEE Expert, 1994

The First International Conference on Intelligent Systems for Molecular Biology.

David B. Searls, Jude W. Shavlik, Lawrence Hunter

AI Mag., 1994

Knowledge-Based Artificial Neural Networks.

Geoffrey G. Towell, Jude W. Shavlik

Artif. Intell., 1994

Using Genetic Search to Refine Knowledge-based Neural Networks.

David W. Opitz, Jude W. Shavlik

Proceedings of the Machine Learning, 1994

Using Sampling and Queries to Extract Rules from Trained Neural Networks.

Mark W. Craven, Jude W. Shavlik

Proceedings of the Machine Learning, 1994

Incorporating Advice into Agents that Learn from Reinforcements.

Richard Maclin, Jude W. Shavlik

Proceedings of the 12th National Conference on Artificial Intelligence, Seattle, WA, USA, July 31, 1994

1993

Extracting Refined Rules from Knowledge-Based Neural Networks.

Geoffrey G. Towell, Jude W. Shavlik

Mach. Learn., 1993

Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding.

Richard Maclin, Jude W. Shavlik

Mach. Learn., 1993

Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools.

Kevin J. Cherkauer, Jude W. Shavlik

Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology, 1993

Heuristically Expanding Knowledge-Based Neural Networks.

David W. Opitz, Jude W. Shavlik

Proceedings of the 13th International Joint Conference on Artificial Intelligence. Chambéry, France, August 28, 1993

Learning to Represent Codons: A Challenge Problem for Constructive Induction.

Mark W. Craven, Jude W. Shavlik

Proceedings of the 13th International Joint Conference on Artificial Intelligence. Chambéry, France, August 28, 1993

Learning Symbolic Rules Using Artificial Neural Networks.

Mark W. Craven, Jude W. Shavlik

Proceedings of the Machine Learning, 1993

1992

Refining PID Controllers Using Neural Networks.

Gary M. Scott, Jude W. Shavlik, W. Harmon Ray

Neural Comput., 1992

Visualizing Learning and Computation in Artificial Neural Networks.

Mark W. Craven, Jude W. Shavlik

Int. J. Artif. Intell. Tools, 1992

Using Symbolic Learning to Improve Knowledge-Based Neural Networks.

Geoffrey G. Towell, Jude W. Shavlik

Proceedings of the 10th National Conference on Artificial Intelligence, 1992

1991

Symbolic and Neural Learning Algorithms: An Experimental Comparison.

Jude W. Shavlik, Raymond J. Mooney, Geoffrey G. Towell

Mach. Learn., 1991

Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules.

Geoffrey G. Towell, Jude W. Shavlik

Proceedings of the Advances in Neural Information Processing Systems 4, 1991

Refined PID Controllers Using Neural Networks.

Gary M. Scott, Jude W. Shavlik, W. Harmon Ray

Proceedings of the Advances in Neural Information Processing Systems 4, 1991

Constructive Induction in Knowledge-Based Neural Networks.

Geoffrey G. Towell, Mark W. Craven, Jude W. Shavlik

Proceedings of the Eighth International Workshop (ML91), 1991

Refining Domain Theories Expressed as Finite-State Automata.

Richard Maclin, Jude W. Shavlik

Proceedings of the Eighth International Workshop (ML91), 1991

1990

Acquiring Recursive and Iterative Concepts with Explanation-Based Learning.

Jude W. Shavlik

Mach. Learn., 1990

Learning in Mathematically-Based Domains: Understanding and Generalizing Obstacle Cancellations.

Jude W. Shavlik, Gerald DeJong

Artif. Intell., 1990

Training Knowledge-Based Neural Networks to Recognize Genes.

Michiel O. Noordewier, Geoffrey G. Towell, Jude W. Shavlik

Proceedings of the Advances in Neural Information Processing Systems 3, 1990

Refinement ofApproximate Domain Theories by Knowledge-Based Neural Networks.

Geoffrey G. Towell, Jude W. Shavlik, Michiel O. Noordewier

Proceedings of the 8th National Conference on Artificial Intelligence. Boston, Massachusetts, USA, July 29, 1990

1989

Acquiring Recursive Concepts with Explanation-Based Learning.

Jude W. Shavlik

Proceedings of the 11th International Joint Conference on Artificial Intelligence. Detroit, 1989

An Experimental Comparison of Symbolic and Connectionist Learning Algorithms.

Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell, Alan Gove

Proceedings of the 11th International Joint Conference on Artificial Intelligence. Detroit, 1989

Combining Explanation-Based Learning and Artificial Neural Networks.

Jude W. Shavlik, Geoffrey G. Towell

Proceedings of the Sixth International Workshop on Machine Learning (ML 1989), 1989

An Empirical Analysis of EBL Approaches for Learning Plan Schemata.

Jude W. Shavlik

Proceedings of the Sixth International Workshop on Machine Learning (ML 1989), 1989

Enriching Vocabularies by Generalizing Explanation Structures.

Richard Maclin, Jude W. Shavlik

Proceedings of the Sixth International Workshop on Machine Learning (ML 1989), 1989

Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems.

Douglas H. Fisher, Kathleen B. McKusick, Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell

Proceedings of the Sixth International Workshop on Machine Learning (ML 1989), 1989

1988

Generalizing the Structure of Explanations in Explanation-Based Learning

Jude W. Shavlik

PhD thesis, 1988

1987

An Explanation-based Approach to Generalizing Number.

Jude W. Shavlik, Gerald DeJong

Proceedings of the 10th International Joint Conference on Artificial Intelligence. Milan, 1987

BAGGER: An EBL System that Extends and Generalizes Explanations.

Jude W. Shavlik, Gerald DeJong

Proceedings of the 6th National Conference on Artificial Intelligence. Seattle, 1987

1986

Computer understanding and generalization of symbolic mathematical calculations: a case study in physics problem solving.

Jude W. Shavlik, Gerald DeJong

Proceedings of the Symposium on Symbolic and Algebraic Manipulation, 1986

1985

Learning about Momentum Conservation.

Jude W. Shavlik

Proceedings of the 9th International Joint Conference on Artificial Intelligence. Los Angeles, 1985