Artificial Intelligence Frontiers in Statistics

D.J. Hand is Professor of Statistics at the Open University, UK.

"This is an interesting collection of specialised papers in a new field. Good AI libraries and all research groups will need a copy."-Computing"The book provides a broad update of current work tat should aid researchers on both sides of the AI/Statistics interface, and is a useful successor to earlier volumes in the series from W A Gale."-Short Book Reviews"...the book is a substantial and worthwhile contribution to the literature for both AI and Statistics. It collects a lot of major results from the frontiers of both sciences and presents them in a very readable context...I enjoyed reading this book very much."-Engineering Applications in Artificial Intelligence

• List of contributors Introduction D.J. Hand -- PART ONE Statistical expert systems -- 1 DEXPERT: an expert system for the design of experiments /T.J. Lorenzen, L.T. Truss, W.S. Spangler, W.T. Corpus and A.B. Parker -- 2 Inside two commercially available statistical expert systems /J.F.M. Raes -- 3 AMIA: Aide a la Modelisation par l'lntelligence Artificielle (expert system for simulation modelling and sectoral forecasting) /M. Ollivier, R. Arrus, JML-A. Durillon, S. Robert and B. Debord -- 4 An architecture for knowledge-based statistical support systems /A. Prat, E. Edmonds, J.M. Catot, J. Lores, J. Galmes and P. Fletcher -- 5 Enhancing explanation capabilities of statistical expert systems through hypertext /P. Hietala -- 6 Measurement scales as metadata D.J. Hand /PART TWO Belief networks -- 7 On the design of belief networks for knowledge-based systems /B. Abramson -- 8 Lack-of-information based control in graphical belief systems -- 9 Adaptive importance sampling for Bayesian networks applied to filtering problems /A.R. Runnalls -- 10 Intelligent arc addition, belief propagation and utilization of parallel processors by probabilistic inference engines /A. Ranjbar and M. McLeish -- 11 A new method for representing and solving Bayesian decision problems /P.P. Shenoy -- PART THREE Learning -- 12 Inferring causal structure in mixed populations /C. Glymour, P. Spirtes and R. Scheines -- 13 A knowledge acquisition inductive system guided by empirical interpretation of derived results /K. Tsujino and S. Nishida -- 14 Incorporating statistical techniques into empirical symbolic learning systems /F. Esposito, D. Malerba and G. Semeraro -- 15 Learning classification trees /W. Buntine -- 16 An analysis of two probabilistic model induction techniques /S.L. Crawford and M. Fung -- PART FOUR Neural networks -- 17 A robust back propagation algorithm for function approximation /D.S. Chen and R.C. Jain -- 18 Maximum likelihood training of neural networks /H. Gish -- 19 A connectionist knowledge acquisition tool: CONKAT /A. Ultsch, R. Mantyk and G. Halmans -- 20 Connectionist, rule-based, and Bayesian decision aids: an empirical comparison /S. Schwartz, J. Wiles, I. Gough and S. Phillips -- PART FIVE Text manipulation -- 21 Statistical approaches to aligning sentences and identifying word correspondences in parallel texts: a report on work in progress /W.A. Gale and K.W. Church -- 22 Probabilistic text understanding /R.P. Goldman and E. Charniak -- 23 The application of machine learning techniques in subject classification /I. Kavanagh, C. Ward and J. Dunnion -- PART SIX Other areas -- 24 A statistical semantics for causation /J. Pearl and T.S. Verma -- 25 Admissible stochastic complexity models for classification problems /P. Smyth -- 26 Combining the probability judgements of experts: statistical and artificial intelligence approaches /LA. Cox -- 27 Randomness and independence in non-monotonic reasoning /E. Neufeld -- 28 Consistent regions in probabilistic logic when using different norms /D. Bouchaffra -- 29 A decision theoretic approach to controlling the cost of planning /L. Hartman -- Index.