Equivalence Between Connectionist and Logical Classifiers The goal of this paper is to show that connectionist and logical classifiers are functionally equivalent. Connectionist classifiers are built as a composition between an associative memory and a decision function. The models of the associative memories studied in this paper are multilayer perceptrons and Hopfield models. Being composed with a decision function, the associative memory can always be transformed into a connectionist classifier. The logical classifiers are defined as a set of logical rules that send a space of boolean inputs into a space of boolean outputs. Such logical classifiers may constitute the knowledge base of an expert system. Methods to extract a logical class...
The construction of computational models with provision for effective learning and added reasoning i...
We discuss the computation by neural networks of semantic operators TP determined by propositional l...
Connectionist modeis, commonly referred to as neural networks, are computing models in which large n...
Although the connectionist approach has lead to elegant solutions to a number of problems in cogniti...
AbstractIt is a long-standing and important problem to integrate logic-based systems and connectioni...
AbstractOne facet of the question of integration of Logic and Connectionist Systems, and how these c...
One facet of the question of integration of Logic and Connectionist Systems, and how these can compl...
Abstract: Impetuous development of artificial neural networks makes it possible to transfer many ide...
A consequence relation (CR) relates sets of beliefs to the appropriate conclusions that might be ded...
The overall aim of the paper is to demonstrate that, from a machine learning point of view, connecti...
We discuss the role of perceptron (or threshold) connectives in the context of Description Logic, an...
AbstractThe paper presents a connectionist framework that is capable of representing and learning pr...
Abstract: "Many recent connectionist models can be categorized as associative memories or pattern cl...
Hölldobler and Kalinke showed how, given a propositional logic program P, a 3-layer feedforward arti...
AbstractModal logics are amongst the most successful applied logical systems. Neural networks were p...
The construction of computational models with provision for effective learning and added reasoning i...
We discuss the computation by neural networks of semantic operators TP determined by propositional l...
Connectionist modeis, commonly referred to as neural networks, are computing models in which large n...
Although the connectionist approach has lead to elegant solutions to a number of problems in cogniti...
AbstractIt is a long-standing and important problem to integrate logic-based systems and connectioni...
AbstractOne facet of the question of integration of Logic and Connectionist Systems, and how these c...
One facet of the question of integration of Logic and Connectionist Systems, and how these can compl...
Abstract: Impetuous development of artificial neural networks makes it possible to transfer many ide...
A consequence relation (CR) relates sets of beliefs to the appropriate conclusions that might be ded...
The overall aim of the paper is to demonstrate that, from a machine learning point of view, connecti...
We discuss the role of perceptron (or threshold) connectives in the context of Description Logic, an...
AbstractThe paper presents a connectionist framework that is capable of representing and learning pr...
Abstract: "Many recent connectionist models can be categorized as associative memories or pattern cl...
Hölldobler and Kalinke showed how, given a propositional logic program P, a 3-layer feedforward arti...
AbstractModal logics are amongst the most successful applied logical systems. Neural networks were p...
The construction of computational models with provision for effective learning and added reasoning i...
We discuss the computation by neural networks of semantic operators TP determined by propositional l...
Connectionist modeis, commonly referred to as neural networks, are computing models in which large n...