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Saeed Hadjifaradji

dblp:86/4011 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1995
—ORCID · none

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

Artificial intelligence and machine learning · 2

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
2 papers
Learning theory · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory › computational learning theory
exact learning		 0.011995
Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks · NIPS 1995
Machine learning › Learning theory › online learning
perceptron		 0.011995
Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks · NIPS 1995
Machine learning › Learning theory
PAC learning		 0.011994
Learning Stochastic Perceptrons Under k-Blocking Distributions · NIPS 1994
Machine learning › Learning theory
distributional assumptions		 0.011994
Learning Stochastic Perceptrons Under k-Blocking Distributions · NIPS 1994

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

unimodality analysis · 0.0monotonic activation · 0.0PAC analysis · 0.0
YearPublicationVenuePosition
1995 Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks
Mario Marchand, Saeed Hadjifaradji
 NIPS2
1994 Learning Stochastic Perceptrons Under k-Blocking Distributions
abstract
We present a statistical method that PAC learns the class of stochastic perceptrons with arbitrary monotonic activation func(cid:173) tion and weights Wi E {-I, 0, + I} when the probability distribution that generates the input examples is member of a family that we call k-blocking distributions. Such distributions represent an impor(cid:173) tant step beyond the case where each input variable is statistically independent since the 2k-blocking family contains all the Markov distributions of order k. By stochastic percept ron we mean a per(cid:173) ceptron which, upon presentation of input vector x, outputs 1 with probability fCLJi WiXi - B). Because the same algorithm works for any monotonic (nondecreasing or nonincreasing) activation func(cid:173) tion f on Boolean domain, it handles the well studied cases of sigmolds and the "usual" radial basis functions.
Mario Marchand, Saeed Hadjifaradji
 NIPS2