John Case

According to our database, John Case authored at least 100 papers between 1971 and 2020.

Collaborative distances:

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

2020

Leveraging Iterative Design of Experiments to Innovate in a High-Risk Production Environment: A US Army Aviation Case Study.

Clay Woody, John Case, Dereck Kennedy, John Caddell

Proceedings of the IEEE International Systems Conference, 2020

2018

Effectivity questions for Kleene's recursion theorem.

John Case, Sanjay Jain, Frank Stephan

Theor. Comput. Sci., 2018

2017

Connections Between Inductive Inference and Machine Learning.

John Case, Sanjay Jain

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

2016

Topological separations in inductive inference.

John Case, Timo Kötzing

Theor. Comput. Sci., 2016

Strongly non-U-shaped language learning results by general techniques.

John Case, Timo Kötzing

Inf. Comput., 2016

Non-Obfuscated Unprovable Programs & Many Resultant Subtleties.

John Case, Michael Ralston

Log. Methods Comput. Sci., 2016

Program Size Complexity of Correction Grammars in the Ershov Hierarchy.

John Case, James S. Royer

Proceedings of the Pursuit of the Universal - 12th Conference on Computability in Europe, 2016

2014

Automatic learners with feedback queries.

John Case, Sanjay Jain, Yuh Shin Ong, Pavel Semukhin, Frank Stephan

J. Comput. Syst. Sci., 2014

2013

On the Necessity of U-Shaped Learning.

Lorenzo Carlucci, John Case

Top. Cogn. Sci., 2013

Memory-limited non-U-shaped learning with solved open problems.

John Case, Timo Kötzing

Theor. Comput. Sci., 2013

Automatic functions, linear time and learning.

John Case, Sanjay Jain, Samuel Seah, Frank Stephan

Log. Methods Comput. Sci., 2013

Beyond Rogers' Non-constructively Computable Function.

John Case, Michael Ralston

Proceedings of the Nature of Computation. Logic, Algorithms, Applications, 2013

2012

Program Self-Reference in Constructive Scott Subdomains.

John Case, Samuel E. Moelius

Theory Comput. Syst., 2012

Algorithmic Scientific Inference: Within Our Computable Expected Reality?

John Case

Int. J. Unconv. Comput., 2012

Learning secrets interactively. Dynamic modeling in inductive inference.

John Case, Timo Kötzing

Inf. Comput., 2012

Automatic learning of subclasses of pattern languages.

John Case, Sanjay Jain, Trong Dao Le, Yuh Shin Ong, Pavel Semukhin, Frank Stephan

Inf. Comput., 2012

2011

Rice and Rice-Shapiro Theorems for transfinite correction grammars.

John Case, Sanjay Jain

Math. Log. Q., 2011

Optimal language learning from positive data.

John Case, Samuel E. Moelius

Inf. Comput., 2011

Properties Complementary to Program Self-Reference.

John Case, Samuel E. Moelius

Fundam. Informaticae, 2011

Measuring Learning Complexity with Criteria Epitomizers.

John Case, Timo Kötzing

Proceedings of the 28th International Symposium on Theoretical Aspects of Computer Science, 2011

2010

Connections Between Inductive Inference and Machine Learning.

John Case, Sanjay Jain

Proceedings of the Encyclopedia of Machine Learning, 2010

Iterative learning of simple external contextual languages.

Leonor Becerra-Bonache, John Case, Sanjay Jain, Frank Stephan

Theor. Comput. Sci., 2010

Strongly Non-U-Shaped Learning Results by General Techniques.

John Case, Timo Kötzing

Proceedings of the COLT 2010, 2010

Solutions to Open Questions for Non-U-Shaped Learning with Memory Limitations.

John Case, Timo Kötzing

Proceedings of the Algorithmic Learning Theory, 21st International Conference, 2010

2009

Parallelism increases iterative learning power.

John Case, Samuel E. Moelius

Theor. Comput. Sci., 2009

Characterizing Programming Systems Allowing Program Self-Reference.

John Case, Samuel E. Moelius

Theory Comput. Syst., 2009

Resource Restricted Computability Theoretic Learning: Illustrative Topics and Problems.

John Case

Theory Comput. Syst., 2009

Learning correction grammars.

Lorenzo Carlucci, John Case, Sanjay Jain

J. Symb. Log., 2009

Independence Results for <i>n</i>-Ary Recursion Theorems.

John Case, Samuel E. Moelius

Proceedings of the Fundamentals of Computation Theory, 17th International Symposium, 2009

Difficulties in Forcing Fairness of Polynomial Time Inductive Inference.

John Case, Timo Kötzing

Proceedings of the Algorithmic Learning Theory, 20th International Conference, 2009

2008

Foreword.

John Case, Takeshi Shinohara, Thomas Zeugmann, Sandra Zilles

Theor. Comput. Sci., 2008

U-shaped, iterative, and iterative-with-counter learning.

John Case, Samuel E. Moelius

Mach. Learn., 2008

Non-U-shaped vacillatory and team learning.

Lorenzo Carlucci, John Case, Sanjay Jain, Frank Stephan

J. Comput. Syst. Sci., 2008

When unlearning helps.

Ganesh Baliga, John Case, Wolfgang Merkle, Frank Stephan, Rolf Wiehagen

Inf. Comput., 2008

Optimal Language Learning.

John Case, Samuel E. Moelius

Proceedings of the Algorithmic Learning Theory, 19th International Conference, 2008

Dynamic Modeling in Inductive Inference.

John Case, Timo Kötzing

Proceedings of the Algorithmic Learning Theory, 19th International Conference, 2008

Dynamically Delayed Postdictive Completeness and Consistency in Learning.

John Case, Timo Kötzing

Proceedings of the Algorithmic Learning Theory, 19th International Conference, 2008

2007

Foreword.

Shai Ben-David, John Case, Thomas Zeugmann

Theor. Comput. Sci., 2007

Results on memory-limited U-shaped learning.

Lorenzo Carlucci, John Case, Sanjay Jain, Frank Stephan

Inf. Comput., 2007

Cautious virus detection in the extreme.

John Case, Samuel E. Moelius

Proceedings of the 2007 Workshop on Programming Languages and Analysis for Security, 2007

Feasible Iteration of Feasible Learning Functionals.

John Case, Timo Kötzing, Todd Paddock

Proceedings of the Algorithmic Learning Theory, 18th International Conference, 2007

2006

Learning a subclass of regular patterns in polynomial time.

John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, Thomas Zeugmann

Theor. Comput. Sci., 2006

Generality's price: Inescapable deficiencies in machine-learned programs.

John Case, Keh-Jiann Chen, Sanjay Jain, Wolfgang Merkle, James S. Royer

Ann. Pure Appl. Log., 2006

Memory-Limited U-Shaped Learning.

Lorenzo Carlucci, John Case, Sanjay Jain, Frank Stephan

Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006

2005

On learning to coordinate: random bits help, insightful normal forms, and competency isomorphisms.

John Case, Sanjay Jain, Franco Montagna, Giulia Simi, Andrea Sorbi

J. Comput. Syst. Sci., 2005

2004

Off-Loading Memory to the Environment: A Quantitative Example.

John Case

Minds Mach., 2004

Parsimony hierarchies for inductive inference.

Andris Ambainis, John Case, Sanjay Jain, Mandayam Suraj

J. Symb. Log., 2004

Robust learning--rich and poor.

John Case, Sanjay Jain, Frank Stephan, Rolf Wiehagen

J. Comput. Syst. Sci., 2004

On the classification of recursive languages.

John Case, Efim B. Kinber, Arun Sharma, Frank Stephan

Inf. Comput., 2004

Identifying Clusters from Positive Data

John Case, Sanjay Jain, Eric Martin, Arun Sharma, Frank Stephan

Electron. Colloquium Comput. Complex., 2004

A Polynomial Time Learner for a Subclass of Regular Patterns

John Case, Sanjay Jain, Rüdiger Reischuk, Frank Stephan, Thomas Zeugmann

Electron. Colloquium Comput. Complex., 2004

Computing Entropy for Ortholog Detection.

Hsing-Kuo Pao, John Case

Proceedings of the International Conference on Computational Intelligence, 2004

2002

Control structures in hypothesis spaces: the influence on learning.

John Case, Sanjay Jain, Mandayam Suraj

Theor. Comput. Sci., 2002

Learning to Win Process-Control Games Watching Game-Masters.

John Case, Matthias Ott, Arun Sharma, Frank Stephan

Inf. Comput., 2002

2001

Synthesizing noise-tolerant language learners.

John Case, Sanjay Jain, Arun Sharma

Theor. Comput. Sci., 2001

Predictive learning models for concept drift.

John Case, Sanjay Jain, Susanne Kaufmann, Arun Sharma, Frank Stephan

Theor. Comput. Sci., 2001

Costs of general purpose learning.

John Case, Keh-Jiann Chen, Sanjay Jain

Theor. Comput. Sci., 2001

Synthesizing Learners Tolerating Computable Noisy Data.

John Case, Sanjay Jain

J. Comput. Syst. Sci., 2001

Lattice computers for approximating Euclidean space.

John Case, Dayanand S. Rajan, Anil M. Shende

J. ACM, 2001

Divide and Conquer Machine Learning for a Genomics Analogy Problem (Progress Report).

Ming Ouyang, John Case, Joan Burnside

Proceedings of the Discovery Science, 4th International Conference, DS 2001, Washington, 2001

2000

Vacillatory and BC learning on noisy data.

John Case, Sanjay Jain, Frank Stephan

Theor. Comput. Sci., 2000

Robust Learning Aided by Context.

John Case, Sanjay Jain, Matthias Ott, Arun Sharma, Frank Stephan

J. Comput. Syst. Sci., 2000

Unlearning Helps.

Ganesh Baliga, John Case, Wolfgang Merkle, Frank Stephan

Proceedings of the Automata, Languages and Programming, 27th International Colloquium, 2000

1999

The Power of Vacillation in Language Learning.

John Case

SIAM J. Comput., 1999

Maximal Machine Learnable Classes.

John Case, Mark A. Fulk

J. Comput. Syst. Sci., 1999

Incremental Concept Learning for Bounded Data Mining.

John Case, Sanjay Jain, Steffen Lange, Thomas Zeugmann

Inf. Comput., 1999

The Synthesis of Language Learners.

Ganesh Baliga, John Case, Sanjay Jain

Inf. Comput., 1999

1997

Learning Recursive Functions from Approximations.

John Case, Susanne Kaufmann, Efim B. Kinber, Martin Kummer

J. Comput. Syst. Sci., 1997

On the Classification of Computable Languages.

John Case, Efim B. Kinber, Arun Sharma, Frank Stephan

Proceedings of the STACS 97, 14th Annual Symposium on Theoretical Aspects of Computer Science, Lübeck, Germany, February 27, 1997

1996

Anomalous Learning Helps Succinctness.

John Case, Sanjay Jain, Arun Sharma

Theor. Comput. Sci., 1996

Learnability: Admissible, Co-Finite, and Hypersimple Languages.

Ganesh Baliga, John Case

J. Comput. Syst. Sci., 1996

Machine Induction Without Revolutionary Changes in Hypothesis Size.

John Case, Sanjay Jain, Arun Sharma

Inf. Comput., 1996

Synthesizing Enumeration Techniques for Language Learning.

Ganesh Baliga, John Case, Sanjay Jain

Proceedings of the Ninth Annual Conference on Computational Learning Theory, 1996

1995

Complexity Issues for Vacillatory Function Identification

John Case, Sanjay Jain, Arun Sharma

Inf. Comput., February, 1995

Language Learning with Some Negative Information.

Ganesh Baliga, John Case, Sanjay Jain

J. Comput. Syst. Sci., 1995

Not-So-Nearly-Minimal-Size Program Inference.

John Case, Mandayam Suraj, Sanjay Jain

Proceedings of the Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report, 1995

Machine Induction Without Revolutionary Paradigm Shifts.

John Case, Sanjay Jain, Arun Sharma

Proceedings of the Algorithmic Learning Theory, 6th International Conference, 1995

1994

Refinements of inductive inference by Popperian and reliable machines.

John Case, Sanjay Jain, Suzanne Ngo Manguelle

Kybernetika, 1994

Machine Learning of Higher-Order Programs.

Ganesh Baliga, John Case, Sanjay Jain, Mandayam Suraj

J. Symb. Log., 1994

Spatial/kinematic domain and lattice computers.

John Case, Dayanand S. Rajan, Anil M. Shende

J. Exp. Theor. Artif. Intell., 1994

Infinitary self-reference in learning theory.

John Case

J. Exp. Theor. Artif. Intell., 1994

Vacillatory Learning of Nearly Minimal Size Grammars.

John Case, Sanjay Jain, Arun Sharma

J. Comput. Syst. Sci., 1994

Learning with Higher Order Additional Information.

Ganesh Baliga, John Case

Proceedings of the Algorithmic Learning Theory, 1994

Subrecursive programming systems - complexity and succinctness.

James S. Royer, John Case

Progress in theoretical computer science, Birkhäuser, ISBN: 978-3-7643-3767-4, 1994

1992

Strong separation of learning classes.

John Case, Keh-Jiann Chen, Sanjay Jain

J. Exp. Theor. Artif. Intell., 1992

On Learning Limiting Programs.

John Case, Sanjay Jain, Arun Sharma

Int. J. Found. Comput. Sci., 1992

Representing the Spatial/Kinematic Domain and Lattice Computers.

John Case, Dayanand S. Rajan, Anil M. Shende

Proceedings of the Analogical and Inductive Inference, 1992

1991

Effectivizing Inseparability.

John Case

Math. Log. Q., 1991

1990

Optimally Representing Euclidean Space Discretely for Analogically Simulating Physical Phenomena.

John Case, Dayanand S. Rajan, Anil M. Shende

Proceedings of the Foundations of Software Technology and Theoretical Computer Science, 1990

Anomalous Learning Helps Succinctness (Extended Abstract).

John Case, Sanjay Jain, Arun Sharma

Proceedings of the Algorithmic Learning Theory, First International Workshop, 1990

1989

Convergence to Nearly Minimal Size Grammars by Vacillating Learning Machines.

Sanjay Jain, Arun Sharma, John Case

Proceedings of the Second Annual Workshop on Computational Learning Theory, 1989

1988

The Power of Vacillation.

John Case

Proceedings of the First Annual Workshop on Computational Learning Theory, 1988

1983

Comparison of Identification Criteria for Machine Inductive Inference.

John Case, Carl Smith

Theor. Comput. Sci., 1983

Pseudo-Extending Computable Functions

John Case

Inf. Control., 1983

1982

Machine Inductive Inference and Language Identification.

John Case, Christopher Lynes

Proceedings of the Automata, 1982

1978

The Complexity of Total Order Structures.

Dan Moore, John Case

J. Comput. Syst. Sci., 1978

Anomaly Hierarchies of Mechanized Inductive Inference

John Case, Carl Smith

Proceedings of the 10th Annual ACM Symposium on Theory of Computing, 1978

1976

Sortability and Extensibility of the Graphs of Recursively Enumerable Partial and Total Orders.

John Case

Math. Log. Q., 1976

1974

Periodicity in Generations of Automata.

John Case

Math. Syst. Theory, 1974

1971

A Note on Degrees of Self-Describing Turing Machines.

John Case

J. ACM, 1971