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Anita Wasilewska

dblp:73/591 · DBLP profile ↗
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7ranked-venue papers
2as first author
0since 2021 · last 2004
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 1 first-authorHuman-computer interaction and ubiquitous computing · 2 · 1 first-authorTheory of computation · 2Databases, data management, data science and information retrieval · 1 · 1 first-author

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
1 paper
Knowledge representation and reasoning · 100%
Theoretical computer science
1 paper
Logic in computer science · 100%
Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%
Databases, data mining, and information retrieval
1 paper
Database theory · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › rule learning
inductive learning
0.011993
Interactive Inductive Learning · Int. J. Man Mach. Stud. 1993
Human-robot interaction › robot learning
interactive learning
0.011993
Interactive Inductive Learning · Int. J. Man Mach. Stud. 1993

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

inductive learning · 0.0
YearPublicationVenuePosition
2004 Bayesian network multi-classifiers for protein secondary structure prediction
Víctor Robles, Pedro Larrañaga, José M. Peña 0002, Ernestina Menasalvas Ruiz, María S. Pérez 0001, Vanessa Herves, Anita Wasilewska
Artif. Intell. Medicine7
2002 Data mining - a semantic model
abstract
Data mining techniques applied to decision support in real-life problems require a multi-step process. Inputs and outputs of these steps require some standard format to be followed in order to achieve a useful platform for the execution of data mining algorithms. There is a need to develop a uniform model where every operation can be expressed in a standard way, allowing algorithms to cooperate and to reuse results. We present, first, a common structure for the representation of inter-step results, and second, a model of the operator, i.e. the entity that handles and transforms this common structure according to a basic data mining algorithm.
Covadonga Fernández 0001, Juan F. Martínez, Anita Wasilewska, Michael Hadjimichael, Ernestina Menasalvas Ruiz
FUZZ-IEEE3
1996 Automating Algebraic Proofs in Algebraic Logic
abstract
We present here an effective proof theory that allows one to reason within algebras of algebraic logic in a purely syntactic, algebraic fashion. We demonstrate the effectiveness of the method by discussing our automated proofs of problems and theorem
Jieh Hsiang, Anita Wasilewska
Fundam. Informaticae2
1993 Application of a Rough Set-Based Inductive Learning System
Michael Hadjimichael, Anita Wasilewska
Fundam. Informaticae2
1993 Interactive Inductive Learning
Michael Hadjimichael, Anita Wasilewska
Int. J. Man Mach. Stud.2
1989 Syntactic Decision Procedures in Information Systems
Anita Wasilewska
Int. J. Man Mach. Stud.1
1988 Knowledge Representation Systems: Syntactic Methods
Anita Wasilewska
IPMU1