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Joseph Peter Costello

dblp:134/3639 · DBLP profile ↗
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5ranked-venue papers
2as first author
0since 2021 · last 1971
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 4 · 2 first-authorSystems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
3 papers
Probabilistic and Bayesian machine learning · 46% Learning paradigms · 27% Optimization for machine learning · 15%
Computer networks
1 paper
Physical-layer communications · 100%
Theoretical computer science
1 paper
Combinatorics and discrete mathematics · 100%

Topics — the 8 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning paradigms
unsupervised learning		 0.021970
Decision-Directed Estimation of a Two-Class Decision Boundary · IEEE Trans. Computers 1970
Asymptotic probability of error using two decision-directed estimators for two unknown mean vectors (Corresp.) · IEEE Trans. Inf. Theory 1968
Physical-layer communications › interference
intersymbol interference		 0.011971
Unsupervised estimation of signals with intersymbol interference (Corresp.) · IEEE Trans. Inf. Theory 1971
Machine learning › Probabilistic and Bayesian machine learning
decision boundary estimation		 0.011970
Decision-Directed Estimation of a Two-Class Decision Boundary · IEEE Trans. Computers 1970
Machine learning › Probabilistic and Bayesian machine learning › structured models › latent variable model
mixture model		 0.011970
Decision-Directed Estimation of a Two-Class Decision Boundary · IEEE Trans. Computers 1970
Machine learning › Optimization for machine learning
stochastic approximation		 0.011970
On unsupervised estimation algorithms · IEEE Trans. Inf. Theory 1970
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › parameter estimation
unsupervised estimation		 0.011970
On unsupervised estimation algorithms · IEEE Trans. Inf. Theory 1970
Machine learning › Learning theory › generalization error
asymptotic error probability		 0.011968
Asymptotic probability of error using two decision-directed estimators for two unknown mean vectors (Corresp.) · IEEE Trans. Inf. Theory 1968
Medical and health informatics › clinical prediction
diagnosis prediction		 0.011970
Decision-Directed Estimation of a Two-Class Decision Boundary · IEEE Trans. Computers 1970

Methods — techniques the papers use, named apart from their topics

regression function · 0.0rate of convergence analysis · 0.0hybrid computer implementation · 0.0digital computer solution · 0.0decision-directed estimation · 0.0
YearPublicationVenuePosition
1971 On the number of points in regular discrete simplex (Corresp.)
Joseph Peter Costello
 IEEE Trans. Inf. Theory1
1971 Unsupervised estimation of signals with intersymbol interference (Corresp.)
abstract
A decision-directed unsupervised estimation algorithm is used in a receiver that processes signals with unknown intersymbol interference. The estimator utilizes the statistical structure of this dependent sample problem to reduce the computational complexity. Asymptotic and experimental dynamic probability of error results are presented.
Joseph Peter Costello, Edward A. Patrick
 IEEE Trans. Inf. Theory1
1970 Decision-Directed Estimation of a Two-Class Decision Boundary
abstract
A hybrid computer is described for implementing decision-directed estimators for the two-class unsupervised estimation problem. Extensions of the system design to the multiclass problem has significant application to computerized medical diagnosis and other problems in communications and pattern recognition. These problems include resolving multiple target bearings in sonar and radar, clustering data such as arise in cell processing, locating modes in density functions for use in interactive data analysis systems, data compression, elimination of intersymbol interference, and many others.
Edward A. Patrick, Joseph Peter Costello, Fabian C. Monds
 IEEE Trans. Computers2
1970 On unsupervised estimation algorithms
abstract
There are several approaches to unsupervised estimation that have application to problems of communications, control, and pattern recognition. This paper presents properties of several different digitally implemented algorithms suitable for unsupervised estimation. One result is the rate of convergence in mean square of the Bayes solution for a discretized parameter space. A regression function that is the expected value of the natural logarithm of the mixture probability density function naturally arises from the Bayes approach. This regression function can be used to devise unsupervised estimation algorithms of the stochastic approximation form. Also, the asymptotic solution and rates of convergence in mean square of a class of minimum-integral-square-difference algorithms are determined. Two other estimators that use a "net" on the parameter space are also presented.
Edward A. Patrick, Joseph Peter Costello
 IEEE Trans. Inf. Theory2
1968 Asymptotic probability of error using two decision-directed estimators for two unknown mean vectors (Corresp.)
abstract
The asymptotic probability of error for an unsupervised learning system with two unknown mean vectors, which are estimated by "decision-directed" estimators, is investigated. Theoretical solutions are obtained for various class probabilities and signal-to-noise ratios by utilizing a digital computer.
Edward A. Patrick, Joseph Peter Costello
 IEEE Trans. Inf. Theory2