Jude W. Shavlik

Jude W. Shavlik's AcademicInfluence.com Rankings

Jude W. Shavlik

Computer Science

#6836

World Rank

#7202

Historical Rank

Artificial Intelligence

#2627

World Rank

#2668

Historical Rank

Database

#3917

World Rank

#4075

Historical Rank

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Computer Science

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Jude W. Shavlik's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Knowledge-Based Artificial Neural Networks (1994) (805)
Extracting Tree-Structured Representations of Trained Networks (1995) (712)
Refinement of approximate domain theories by knowledge-based neural networks (1990) (433)
Readings in Machine Learning (1991) (382)
Using Sampling and Queries to Extract Rules from Trained Neural Networks (1994) (360)
Generating Accurate and Diverse Members of a Neural-Network Ensemble (1995) (359)
Actively Searching for an Effective Neural Network Ensemble (1996) (340)
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS (2011) (303)
Using neural networks for data mining (1997) (289)
Extracting Refined Rules from Knowledge-Based Neural Networks (1993) (282)
Extracting comprehensible models from trained neural networks (1996) (269)
Extracting refined rules from knowledge-based neural networks (2004) (261)
THE EXTRACTION OF REFINED RULES FROM KNOWLEDGE BASED NEURAL NETWORKS (1993) (247)
Corleone: hands-off crowdsourcing for entity matching (2014) (240)
Learning users' interests by unobtrusively observing their normal behavior (2000) (218)
DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference (2012) (207)
Knowledge-Based Support Vector Machine Classifiers (2002) (200)
Training Knowledge-Based Neural Networks to Recognize Genes (1990) (200)
An Experimental Comparison of Symbolic and Connectionist Learning Algorithms (1989) (180)
Creating Advice-Taking Reinforcement Learners (1998) (169)
Gradient-based boosting for statistical relational learning: The relational dependency network case (2011) (148)
Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules (1991) (144)
Symbolic and neural learning algorithms: An experimental comparison (2004) (142)
An Approach to Combining Explanation-based and Neural Learning Algorithms (1989) (128)
Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks (1995) (127)
Learning Symbolic Rules Using Artificial Neural Networks (1993) (123)
Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another (2005) (114)
A Bayesian Network Approach to Operon Prediction (2003) (114)
Giving Advice about Preferred Actions to Reinforcement Learners Via Knowledge-Based Kernel Regression (2005) (107)
Knowledge-Based Kernel Approximation (2004) (105)
Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference (2012) (102)
Learning Markov Logic Networks via Functional Gradient Boosting (2011) (100)
Breast cancer risk estimation with artificial neural networks revisited (2010) (89)
Chapter 11 Transfer Learning (2009) (89)
Symbolic and Neural Learning Algorithms: An Experimental Comparison (1991) (85)
Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction (2004) (85)
A Probabilistic Learning Approach to Whole-Genome Operon Prediction (2000) (84)
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies (1997) (81)
Growing Simpler Decision Trees to Facilitate Knowledge Discovery (1996) (80)
Incorporating Advice into Agents that Learn from Reinforcements (1994) (79)
Speeding Up Inference in Markov Logic Networks by Preprocessing to Reduce the Size of the Resulting Grounded Network (2009) (79)
A Framework for Combining Symbolic and Neural Learning (1992) (79)
Skill Acquisition Via Transfer Learning and Advice Taking (2006) (70)
Rule Extraction: Where Do We Go from Here? (1999) (69)
Multi-Agent Inverse Reinforcement Learning (2010) (69)
Relational Data Mining with Inductive Logic Programming for Link Discovery (2002) (68)
Knowledge-Based Nonlinear Kernel Classifiers (2003) (66)
Machine Learning: Proceedings of the Fifteenth International Conference (1998) (64)
Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage (2004) (64)
Relational Macros for Transfer in Reinforcement Learning (2007) (63)
Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach (2011) (62)
Refining PID Controllers Using Neural Networks (1991) (61)
Information Extraction for Clinical Data Mining: A Mammography Case Study (2009) (61)
Visualizing Learning and Computation in Artificial Neural Networks (1992) (58)
Using Machine Learning to Design and Interpret Gene-Expression Microarrays (2004) (57)
Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves (2006) (57)
View Learning for Statistical Relational Learning: With an Application to Mammography (2005) (57)
Combining Symbolic and Neural Learning (1994) (51)
Big Data versus the Crowd: Looking for Relationships in All the Right Places (2012) (51)
A System for Building Intelligent Agents that Learn to Retrieve and Extract Information (2003) (51)
Dynamically adding symbolically meaningful nodes to knowledge-based neural networks (1995) (49)
The Extraction of Reened Rules from Knowledge-based Neural Networks (1993) (48)
Constructive Induction in Knowledge-Based Neural Networks (1991) (48)
Acquiring Recursive and Iterative Concepts with Explanation-Based Learning (1990) (46)
Machine learning approaches to gene recognition (1994) (44)
Heuristically Expanding Knowledge-Based Neural Networks (1993) (44)
Understanding Time-Series Networks: A Case Study in Rule Extraction (1997) (44)
Re nement of Approximate Domain Theories byKnowledge-Based Neural Networks (1990) (42)
Mirror Descent for Metric Learning: A Unified Approach (2012) (41)
Evaluating machine learning approaches for aiding probe selection for gene-expression arrays (2002) (41)
An instructable, adaptive interface for discovering and monitoring information on the World-Wide Web (1998) (39)
Intelligent Agents for Web-based Tasks: An Advice-Taking Approach (1998) (38)
Using Symbolic Learning to Improve Knowledge-Based Neural Networks (1992) (36)
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases (2015) (35)
Support Vector Machines for Differential Prediction (2014) (34)
Guiding Autonomous Agents to Better Behaviors through Human Advice (2013) (33)
Using knowledge-based neural networks to improve algorithms: Refining the Chou-Fasman algorithm for protein folding (1993) (33)
BAGGER: An EBL System that Extends and Generalizes Explanations (1987) (32)
An Explanation-based Approach to Generalizing Number (1987) (31)
An Empirical Evaluation of Bagging in Inductive Logic Programming (2002) (31)
Creating protein models from electron-density maps using particle-filtering methods (2007) (30)
Refining algorithms with knowledge-based neural networks: improving the Chou-Fasman algorithm for protein folding (1994) (29)
Learning to Represent Codons: A Challenge Problem for Constructive Induction (1993) (29)
Scaling Inference for Markov Logic via Dual Decomposition (2012) (29)
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology (1993) (28)
Using Genetic Search to Refine Knowledge-based Neural Networks (1994) (27)
Online Knowledge-Based Support Vector Machines (2010) (27)
Deep Learning Powered In-Session Contextual Ranking using Clickthrough Data (2016) (26)
Acquiring Recursive Concepts with Explanation-Based Learning (1989) (26)
Automatically Labeling Web Pages Based on Normal User Actions (1999) (26)
Transfer Learning via Advice Taking (2010) (26)
A probabilistic approach to protein backbone tracing in electron density maps (2006) (26)
Computational methods for fast and accurate dna fragment assembly (1999) (24)
Creating advice-taking reinforcement learners (2004) (24)
Finding Genes by Case-Based Reasoning in the Presence of Noisy Case Boundaries * (1991) (23)
Combining Explanation-Based Learning and Artificial Neural Networks (1989) (22)
Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine (2015) (22)
Bellwether analysis: predicting global aggregates from local regions (2006) (22)
Transfer in Reinforcement Learning via Markov Logic Networks (2008) (20)
Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou–Fasman Algorithm for Protein Folding (1992) (20)
Knowledge-Based Support-Vector Regression for Reinforcement Learning (2005) (20)
Boosting relational dependency networks (2010) (20)
Extending Explanation-Based Learning by Generalizing the Structure of Explanations (1990) (19)
A Genetic Algorithm Approach for Creating Neural-Network Ensembles (1999) (19)
Learning about Momentum Conservation (1985) (19)
A Theory-Refinement Approach to Information Extraction (2001) (19)
A Simple and Effective Method for Incorporating Advice into Kernel Methods (2006) (19)
Uplift Modeling with ROC: An SRL Case Study (2013) (18)
Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models (2010) (18)
An Empirical Evaluation of Machine Learning Approaches for Angry Birds (18)
Building Intelligent Agents for Web-Based Tasks: A Theory-Refinement Approach (1998) (18)
Felix: Scaling Inference for Markov Logic with an Operator-based Approach (2011) (18)
Neural network input representations that produce accurate consensus sequences from DNA fragment assemblies (1999) (18)
EXPERIMENTAL ANALYSIS OF ASPECTS OF THE CASCADE-CORRELATION LEARNING ARCHITECTURE (1991) (18)
Learning to Extract Genic Interactions Using Gleaner (2005) (18)
Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer (2009) (18)
Learning an Approximation to Inductive Logic Programming Clause Evaluation (2004) (17)
Bellwether analysis: Searching for cost-effective query-defined predictors in large databases (2009) (17)
Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools (1993) (17)
Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming (2010) (17)
Evaluating Software Sensors for Actively Profiling Windows 2000 Computer Users (2001) (16)
Relational One-Class Classification: A Non-Parametric Approach (2014) (16)
Refining Domain Theories Expressed as Finite-State Automata (1991) (15)
Acquiring Special Case Schemata in Explanation-Based Learning (1987) (15)
The First International Conference on Intelligent Systems for Molecular Biology (1994) (15)
Using neural networks to refine existing biological knowledge (1992) (15)
Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms (2013) (15)
Toward Automatic Management of Embarrassingly Parallel Applications (2003) (15)
Policy Transfer via Markov Logic Networks (2009) (14)
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems (1989) (14)
Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems (2015) (13)
Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge (2010) (13)
Integrating machine learning and physician knowledge to improve the accuracy of breast biopsy. (2011) (13)
Computational Learning Theory and Natural Learning (1996) (12)
Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling (2013) (12)
Refining Rules Incorporated into Knowledge-Based Support Vector Learners Via Successive Linear Programming (2007) (12)
Anomaly Detection in Text: The Value of Domain Knowledge (2015) (12)
Improving the Quality of Automatic DNA Sequence Assembly Using Fluorescent Trace-Data Classifications (1996) (11)
Combining Explanation-Based and Neural Learning: An Algorithm and Empirical Results (1989) (11)
Applying Theory Revision to the Design of Distributed Databases (2003) (11)
Interpreting microarray expression data using text annotating the genes (2002) (10)
Acquiring recursive and iterative concepts with explanation-based learning (2004) (10)
Intelligent Web Agents that Learn to Retrieve and Extract Information (2003) (10)
Belief Propagation in Large, Highly Connected Graphs for 3D Part-Based Object Recognition (2006) (10)
Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge (2014) (9)
Detecting Semantic Uncertainty by Learning Hedge Cues in Sentences Using an HMM (2014) (9)
Boosted Statistical Relational Learners (2014) (9)
Rapid Quality Estimation of Neural Network Input Representations (1995) (8)
An Overview of Research at Wisconsin on Knowledge-Based Neural Networks (1996) (8)
Improved Methods for Template-Matching in Electron-Density Maps Using Spherical Harmonics (2007) (8)
Building Relational World Models for Reinforcement Learning (2007) (8)
Relational transfer in reinforcement learning (2009) (8)
Spherical-harmonic decomposition for molecular recognition in electron-density maps (2009) (8)
ANALYZING VARIABLE CANCELLATIONS TO GENERALIZE SYMBOLIC MATHEMATICAL CALCULATIONS. (1986) (8)
Selecting Salient Features for Machine Learning from Large Candidate Pools through Parallel Decision (1994) (8)
Learning Relational Dependency Networks for Relation Extraction (2016) (8)
A Model of Attention Focussing During Problem Solving (1986) (7)
Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes (2000) (6)
Instructable and Adaptive Web Agents that Learn to Retrieve and Extract Information (2000) (6)
Learning classical physics (1986) (6)
Rapidly Estimating the Quality of Input Representations for Neural Networks (1995) (6)
Using knowledge-based neural networks to refine existing biological theories (1993) (6)
Advice Taking and Transfer Learning: Naturally Inspired Extensions to Reinforcement Learning (2008) (6)
The Adviceptron: Giving Advice to the Perceptron (2010) (6)
Biomedical Informatics Training at the University of Wisconsin-Madison (2007) (5)
Using Multiple Levels of Learning and Diverse Evidence Sources to Uncover Coordinately Controlled Genes (2000) (5)
Integrating knowledge capture and supervised learning through a human-computer interface (2011) (5)
Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming (2007) (5)
Acquiring general iterative concepts by reformulating explanations observed examples (1990) (5)
Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations (1997) (5)
Computer understanding and generalization of symbolic mathematical calculations: a case study in physics problem solving (1986) (5)
A self-tuning method for one-chip SNP identification (2004) (5)
Learning from Human Teachers: Issues and Challenges for ILP in Bootstrap Learning (2010) (5)
Improving the Efficiency of Belief Propagation in Large, Highly Connected Graphs (2006) (5)
Advice Refinement in Knowledge-Based SVMs (2011) (5)
An Empirical Analysis of EBL Approaches for Learning Plan Schemata (1989) (5)
Using machine learning to identify benign cases with non-definitive biopsy (2013) (4)
Knowledge transfer via advice taking (2005) (4)
Case-Based Reasoning with Noisy Case Boundaries: An Application in Molecular Biology (1990) (4)
Rule Extraction for Transfer Learning (2008) (4)
Guiding belief propagation using domain knowledge for protein-structure determination (2010) (4)
Learning Relational Probabilistic Models from Partially Observed Data-Opening the Closed-World Assumption (2013) (4)
Extracting Thee-Structured Representations of Thained Networks (1995) (4)
Learning a New View of a Database: With an Application to Mammography (2007) (4)
Learning by symbolic and neural methods (1998) (3)
Probabilistic ensembles for improved inference in protein-structure determination (2011) (3)
Predictive Models in Personalized Medicine: Neural Information Processing Systems (NIPS), 2010 workshop report (2011) (3)
Learning ensembles of first-order clauses that optimize precision-recall curves (2007) (3)
ILP for Bootstrapped Learning: A Layered Approach to Automating the ILP Setup Problem (2009) (3)
Advice Refinement in Knowledge-Based Support Vector Machines (2007) (3)
Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates (2007) (3)
Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule (2009) (3)
Selection, Combination, and Evaluation of Effective Software Sensors for Detecting Abnormal Usage of Computers Running Windows NT/2000 (2002) (2)
Refinement ofApproximate Domain Theories by Knowledge-Based Neural Networks (1990) (2)
Pictorial Structures for Molecular Modeling: Interpreting Density Maps (2004) (2)
Structure Learning with Hidden Data in Relational Domains (2012) (2)
Speeding Up Relational Data Mining by Learning to Estimate Candidate Hypothesis Scores (2003) (2)
Estimating Users ’ Interest in Web Pages by Unobtrusively Monitoring Users ’ Normal Behavior (2000) (2)
Learning Relational Structure for Temporal Relation Extraction (2012) (2)
Using Explanation-Based Learning to Acquire Programs by Analyzing Examples (1989) (2)
Smooth Support Vector Machines (2007) (2)
Proceedings, Third IEEE International Conference on Data Mining, ICDM 2003, 19-22 November 2003, Melbourne, Florida (2003) (2)
Guest editors’ introduction: special issue on inductive logic programming (ILP-2007) (2008) (2)
Combining symbolic and neural learning (2004) (2)
Using Heuristic Search to Expand Knowledge-Based Neural Networks (1995) (2)
Adaptively finding and combining first-order rules for large, skewed data sets (2009) (2)
Elementary (2012) (1)
Empirically Evaluating EBL (1993) (1)
A Domain-Independent Approach (1990) (1)
Proceedings of the 17th international conference on Inductive logic programming (2007) (1)
Scaling Inference for Markov Logic with a Task-Decomposition Approach (2011) (1)
Pictorial structures for molecular modeling (2005) (1)
Generalizing Explanation Structures (1993) (1)
Inductive Logic Programming, 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers (2008) (1)
A Recap of Early Work on Theory and Knowledge Refinement (2021) (1)
Refining PIn Controllers using Neural Networks (1991) (1)
Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, July 24-27, 1998 (1998) (1)
Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text (2013) (1)
Using neural networks to automatically refine expert system knowledge bases: experiments in the NYNEX MAX domain (1997) (1)
Broadening the applicability of relational learning (2011) (1)
Advice-based Transfer in Reinforcement Learning (1)
Using a Trained Text Classifier to Extract Information (1999) (1)
Bootstrapping Knowledge Base Acceleration (2013) (1)
Learning in Mathematically-Based Domains (1990) (1)
Boosting in the Presence of Missing Data (2014) (1)
Imitation Learning in Relational Domains Using Functional Gradient Boosting (1)
MIRROR DESCENT FOR METRIC LEARNING (2010) (1)
Boosting First-Order Clauses for Large, Skewed Data Sets (2009) (1)
A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment (2005) (1)
TAC KBP 2015 : English Slot Filling Track Relational Learning with Expert Advice (2015) (1)
Creating Robust Relation Extract and Anomaly Detect via Probabilistic Logic-Based Reasoning and Learning (2017) (1)
Bridging science and applications (panel 1) (1998) (0)
Refined PID Controllers Using Neural Networks (1991) (0)
CHAPTER THIRTY-SEVEN – Finding Frameshift Errors in Anonymous DNA (1994) (0)
Félix (2020) (0)
Report on the First International Conference on Knowledge Capture (K-CAP) (2002) (0)
Enriching Vocabularies by Generalizing Explanation Structures (1989) (0)
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology, Bethesda, MD, USA, July 1993 (1993) (0)
Case Study 3 — PHYSICS 101: Learning in Mathematically Based Domains (1993) (0)
Selecting good models (1995) (0)
Invited Talks (2019) (0)
Relational Learning (2014) (0)
ONLINE LEARNING WITH KNOWLEDGE-BASED SUPPORT VECTOR MACHINES (2006) (0)
Introduction (2004) (0)
Knowledge-Intensive, Interactive and Efficient Relational Pattern Learning (2006) (0)
Relational Learning With Expert Advice (2015) (0)
Faust: Flexible Acquistion and Understanding System for Text (2013) (0)
13 CREATING ADVICE-TAKING REINFORCEMENT LEARNERS (0)
ISMB-93 : proceedings : first international conference on intelligent systems for molecular biology (1993) (0)
Sifting and winnowing: approaches to finding useful information on the web (2003) (0)
3.3.2 Site-speciic Information (0)
Table of Contents (2003) (0)
ICMLA 2008 Invited Speakers (2008) (0)
Classification from One Class of Examples for Relational Domains (2014) (0)
Chapter 1 Machine Learning in Structural Biology : Interpreting 3 D Protein Images (2006) (0)
Boosting (Bi-)Directed Relational Models (2014) (0)
Boosting Statistical Relational Learning in Action (2014) (0)
Twenty-Five Years of Combining Symbolic and Numeric Learning (2012) (0)
Applying machine learning techniques to DNA sequence analysis (1992) (0)
Scaling Up ILP: Experiences with Extracting Relations from Biomedical Text (2004) (0)
Advising your Adaptive Software Agent ( extended abstract ) (2002) (0)
An Empirical Analysis of Explanation-Based Learning (1990) (0)
Applying machine learning techniques to DNA sequence analysis. Progress report, Year 2, February 14, 1992--December 11, 1992 (1992) (0)
Using Pictorial Structures to Identify Proteins in X-ray Crystallographic Electron Density Maps (2003) (0)
Some Aspects of Operationality (1993) (0)
Contributors to the Challenge Task (0)
Online Learning with Knowledge-Based SVMs (2009) (0)
Applying machine learning techniques to DNA sequence analysis. Progress report, February 14, 1991--February 13, 1992 (1992) (0)
Hybrid Symbolic-Neural Methods for Improved Recognition Using High-Level Visual Features (1992) (0)
Using knowledge-based neural networks to refine roughly-correct information (1994) (0)
Gradient-based boosting for statistical relational learning: The relational dependency network case (2011) (0)
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases (2015) (0)
Felix : Scaling up Global Statistical Information Extraction Using an Operator-based Approach (2011) (0)
Boosting Undirected Relational Models (2014) (0)

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What Schools Are Affiliated With Jude W. Shavlik?

Jude W. Shavlik is affiliated with the following schools:

University of Wisconsin–Madison

Jude W. Shavlik's Academic­Influence.com Rankings

Why Is Jude W. Shavlik Influential?

Jude W. Shavlik's Published Works

Published Works

What Schools Are Affiliated With Jude W. Shavlik?

Jude W. Shavlik's AcademicInfluence.com Rankings