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Jonathan P. E. Hodgson
St. Joseph's University
Philadelphia, Pennsylvania
 

Dr. Jonathan Hodgson is a Professor of Mathematics and Computer Science at Saint Joseph's University in Philadelphia, PA, where he teaches a wide variety of courses at both the undergraduate and master's level. Previously, he was on the faculty at Adelphi University and, prior to that, at the University of Pennsylvania. Dr. Hodgson started his career as a mathematician working in the field of topology. He holds a Ph.D. in Mathematics from the University of Cambridge. Dr. Hodgson became involved with computers, originally with the idea of drawing pictures of knots on a Tektronix machine using Plot-90. In the 1980s, he developed an interest in artificial intelligence, particularly problem solving and logic programming, both of which are his areas of major research interest.

Dr. Hodgson has published papers on differential topology, problem solving, and the use of Hypertext Markup Language (HTML) tags for flagging semantic content in Web pages. He is currently the convenor of WG17, the ISO/IEC JTC1 working group on Prolog standardization.


     

Tableau reasoning for description logics and its extension to probabilities
Zese R., Bellodi E., Riguzzi F., Cota G., Lamma E.  Annals of Mathematics and Artificial Intelligence 82(1-3): 101-130, 2018. Type: Article

Zese et al. present Prolog implementations of some reasoning algorithms for description logics (DL). They describe two principal algorithms, TRILL and TRILLp; each implements the tableau algorithm. TRILL is able to re...

 

Sparsification and subexponential approximation
Bonnet E., Paschos V.  Acta Informatica 55(1): 1-15, 2018. Type: Article

A sparsification of a problem maps the problem to a set of similar problems, each of which has its parameters bounded by some fixed number. For example, in the case of a graph-based problem, the degree might be one parameter. The solutions of thes...

 

A Bayesian nonparametric model for multi-label learning
Xuan J., Lu J., Zhang G., Xu R., Luo X.  Machine Learning 106(11): 1787-1815, 2017. Type: Article

Existing generative models for multilabel learning require that the number of topics be fixed in advance. This paper proposes a Bayesian nonparametric model that does not have this requirement....

 

Evaluation of an automated knowledge-based textual summarization system for longitudinal clinical data, in the intensive care domain
Goldstein A., Shahar Y., Orenbuch E., Cohen M.  Artificial Intelligence in Medicine 82 20-33, 2017. Type: Article

This paper examines the potential of the Clinitext system [1] for the production of clinical summaries. Clinitext is designed to be general and not specific to any clinical domain; details on Clinitext are provided in [1]. In particular, the autho...

 

Low-rank decomposition meets kernel learning
Lan L., Zhang K., Ge H., Cheng W., Liu J., Rauber A., Li X., Wang J., Zha H.  Artificial Intelligence 250 1-15, 2017. Type: Article

This paper describes how low-rank kernel learning can be modified to make use of side information such as class labels on some of the data. In low-rank kernel learning, the kernel can be approximated using the Nystrom method in which a selection o...

 
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