Results 1  10
of
347,852
PROBABILISTIC PREDICATE TRANSFORMERS
, 1995
"... Predicate transformers facilitate reasoning about imperative programs, including those exhibiting demonic nondeterministic choice. Probabilistic predicate transformers extend that facility to programs containing probabilistic choice, so that one can in principle determine not only whether a program ..."
Abstract

Cited by 138 (41 self)
 Add to MetaCart
Predicate transformers facilitate reasoning about imperative programs, including those exhibiting demonic nondeterministic choice. Probabilistic predicate transformers extend that facility to programs containing probabilistic choice, so that one can in principle determine not only whether a
Probabilistic predicate transformers: Part 2
 ACM Transactions on Programming Languages and Systems
, 1996
"... Probabilistic predicate transformers guarantee standard (ordinary) predicate transformers to incorporate a notion of probabilistic choice in imperative programs. The basic theory of that, for finite state spaces, is set out in [5], together with a statements of their `healthiness conditions'. H ..."
Abstract

Cited by 11 (7 self)
 Add to MetaCart
Probabilistic predicate transformers guarantee standard (ordinary) predicate transformers to incorporate a notion of probabilistic choice in imperative programs. The basic theory of that, for finite state spaces, is set out in [5], together with a statements of their `healthiness conditions
Learning probabilistic relational models
 In IJCAI
, 1999
"... A large portion of realworld data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
Abstract

Cited by 619 (31 self)
 Add to MetaCart
of the relational structure present in our database. This paper builds on the recent work on probabilistic relational models (PRMs), and describes how to learn them from databases. PRMs allow the properties of an object to depend probabilistically both on other properties of that object and on properties of related
The Semantics of Predicate Logic as a Programming Language
 Journal of the ACM
, 1976
"... ABSTRACT Sentences in firstorder predicate logic can be usefully interpreted as programs In this paper the operational and fixpomt semantics of predicate logic programs are defined, and the connections with the proof theory and model theory of logic are investigated It is concluded that operational ..."
Abstract

Cited by 810 (18 self)
 Add to MetaCart
ABSTRACT Sentences in firstorder predicate logic can be usefully interpreted as programs In this paper the operational and fixpomt semantics of predicate logic programs are defined, and the connections with the proof theory and model theory of logic are investigated It is concluded
Probabilistic Latent Semantic Indexing
, 1999
"... Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized ..."
Abstract

Cited by 1207 (11 self)
 Add to MetaCart
Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized
Probabilistic Principal Component Analysis
 Journal of the Royal Statistical Society, Series B
, 1999
"... Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximumlikelihood estimation of paramet ..."
Abstract

Cited by 703 (5 self)
 Add to MetaCart
of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach
Probabilistic Latent Semantic Analysis
 In Proc. of Uncertainty in Artificial Intelligence, UAI’99
, 1999
"... Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of twomode and cooccurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Sema ..."
Abstract

Cited by 760 (9 self)
 Add to MetaCart
Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of twomode and cooccurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent
The Perceptron: A Probabilistic Model for Information Storage and Organization in The Brain
 Psychological Review
, 1958
"... If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what ..."
Abstract

Cited by 1143 (0 self)
 Add to MetaCart
If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the
Mixtures of Probabilistic Principal Component Analysers
, 1998
"... Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a com ..."
Abstract

Cited by 537 (6 self)
 Add to MetaCart
maximumlikelihood framework, based on a specific form of Gaussian latent variable model. This leads to a welldefined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context
Probabilistic Visual Learning for Object Representation
, 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in highdimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixtureof ..."
Abstract

Cited by 705 (15 self)
 Add to MetaCart
ofGaussians model (for multimodal distributions). These probability densities are then used to formulate a maximumlikelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection
Results 1  10
of
347,852