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- Nir Friedman, Dan Geiger, Moisés Goldszmidt
- Machine Learning
- 1997

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper… (More)

Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inference algorithms. It is well-known, however, that there are certain independencies that we cannot capture… (More)

- Craig Boutilier, Richard Dearden, Moisés Goldszmidt
- Artif. Intell.
- 2000

Markov decision processes (MDPs) have proven to be popular models for decision-theoretic planning, but standard dynamic programming algorithms for solving MDPs rely on explicit, state-based specifications and computations. To alleviate the combinatorial problems associated with such methods, we propose new representational and computational techniques for… (More)

- Nir Friedman, Moisés Goldszmidt
- UAI
- 1996

In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability tables (CPTs), that quantify these networks. This increases the space of possible models, enabling the… (More)

This paper studies the use of statistical induction techniques as a basis for automated performance diagnosis and performance management. The goal of the work is to develop and evaluate tools for offline and online analysis of system metrics gathered from instrumentation in Internet server platforms. We use a promising class of probabilistic models… (More)

- Craig Boutilier, Richard Dearden, Moisés Goldszmidt
- IJCAI
- 1995

Markov decision processes (MDPs) have recently been applied to the problem of modeling decisiontheoretic planning. While traditional methods for solving MDPs are often practical for small states spaces, their effectiveness for large AI planning problems is questionable. We present an algorithm, called structured policy iteration (SPI), that constructs… (More)

We present a method for automatically extracting from a running system an indexable <i>signature</i> that distills the essential characteristic from a system state and that can be subjected to automated clustering and similarity-based retrieval to identify when an observed system state is similar to a previously-observed state. This allows operators to… (More)

- Nir Friedman, Moisés Goldszmidt
- AAAI/IAAI, Vol. 2
- 1996

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper… (More)

- Moisés Goldszmidt, Paul H. Morris, Judea Pearl
- IEEE Trans. Pattern Anal. Mach. Intell.
- 1990

We propose an approach to nonmonotonic reasoning that combines the principle of infinitesimal probabilities with that of maximum entropy, thus extending the inferential power of the probabilistic interpretation of defaults. We provide a precise formalization of the consequences entailed by a conditional knowledge base, develop the computational machinery… (More)

- Moisés Goldszmidt, Judea Pearl
- KR
- 1992