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Searching for authors named James Cussens – sorted by Relevance.

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  • Model equivalence of PRISM programs  
  • by James Cussens
  • …Abstract. The problem of deciding the probability model equivalence of two PRISM programs is addressed. In the finite case this problem can be solved (albeit slowly) using techniques from algebraic statistics, specifically the computation of elimination ideals and Gröbner bases. A very brief introdu…
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  • Stochastic Logic Programs: Sampling, Inference and Applications  
  • by James Cussens — 2000 — In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2000
  • …Algorithms for exact and approximate inference in stochastic logic programs (SLPs) are presented, based respectively, on variable elimination and importance sampling. We then show how SLPs can be used to represent prior distributions for machine learning, using (i) logic programs and (ii) Baye…
  • Cited by 15 (1 self) – Add To MetaCart
  • A Bayesian Analysis of Algorithms for Learning Finite Functions  
  • by James Cussens — 1995 — Machine Learning: Proceedings of the Twelfth International Conference (ML95
  • …We consider algorithms for learning functions f : X ! Y , where X and Y are finite, and there is assumed to be no noise in the data. Learning algorithms, Alg, are connected with \Gamma(Alg), the set of prior probability distributions for which they are optimal. A method for constructing \Gamma(Alg)…
  • Cited by 3 (1 self) – Add To MetaCart
  • Using Prior Probabilities and Density Estimation for Relational Classification  
  • by James Cussens — 1998 — In Proceedings of the Inductive Logic Programming Conference(ILP'98
  • …. A Bayesian method for incorporating probabilistic background knowledge into ILP is presented. Positive only learning is extended to allow density estimation. Estimated densities and defined prior are combined in Bayes theorem to perform relational classification. An initial application of the …
  • Cited by 8 (1 self) – Add To MetaCart
  • Estimating Rule Accuracies from Training Data  
  • by James Cussens — 1992
  • …Our goal is to assess how confident we can be in rules induced from training data, rather than propose how they should be induced in the first place. The standard confirmation-theoretic approach is rejected in favour of estimating the domain accuracies of rules. This is done in both the Classical a…
  • Cited by 1 (0 self) – Add To MetaCart
  • Loglinear Models for First-Order Probabilistic Reasoning  
  • by James Cussens — 1999 — In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence
  • …Recent work on loglinear models in probabilistic constraint logic programming is applied to first-order probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) com…
  • Cited by 26 (3 self) – Add To MetaCart
  • Bayesian network learning by compiling to weighted MAX-SAT  
  • by James Cussens
  • …The problem of learning discrete Bayesian networks from data is encoded as a weighted MAX-SAT problem and the MaxWalkSat local search algorithm is used to address it. For each dataset, the per-variable summands of the (BDeu) marginal likelihood for different choices of parents (‘family scores’) are …
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  • Part-of-Speech Tagging Using Progol  
  • by James Cussens — 1997 — In Inductive Logic Programming: Proceedings of the 7th International Workshop (ILP-97). LNAI 1297
  • …. A system for `tagging' words with their part-of-speech (POS) tags is constructed. The system has two components: a lexicon containing the set of possible POS tags for a given word, and rules which use a word's context to eliminate possible tags for a word. The Inductive Logic Programming (ILP) sys…
  • Cited by 42 (3 self) – Add To MetaCart
  • Parameter Estimation in Stochastic Logic Programs  
  • by James Cussens — 2000 — Machine Learning
  • …. Stochastic logic programs (SLPs) are logic programs with labelled clauses which dene a log-linear distribution over refutations of goals. The loglinear distribution provides, by marginalisation, a distribution over variable bindings, allowing SLPs to compactly represent quite complex distributions…
  • Cited by 47 (3 self) – Add To MetaCart
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