This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied to incompatible ill-posed linear inverse problems. Specifically we introduce a novel stopping rule which defines a regularization algorithm for the Iterative Space Reconstruction Algorithm in the case of Least-Squares minimization. Further we show that the same rule regularizes the Expectation M...

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules, ignoring the interactive nature between embedding learning and logical inference. And they focused only on hard rules, which always hold with...

Previous research has shown that Bidirectional Associative Memories (BAM), a special type of artificial neural network, can perform various types of associations that human beings are able to perform with little effort. However, considering a simple association problem, such as associating faces with names, iterative type BAM networks usually take hundreds and sometimes thousands of learning tr...

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the...

Surprise is informative because it drives attention [1] and modifies learning [2]. Correlates of surprise have been observed at different stages of neural processing, and found to be relevant for learning and memory formation [3]. Although surprise is ubiquitous, there is neither a widely accepted theory that quantitatively links surprise to observed behavior, such as the startle response, nor ...

This paper discusses the linguistic knowledge extraction from the iterative execution of a multiplayer non-cooperative repeated game. Linguistic knowledge is automatically extracted in the form of fuzzy if-then rules. Our knowledge extraction is mainly based on the on-line incremental learning of fuzzy rule-based systems. In this sense, our linguistic knowledge extraction is the learning of fuz...

This paper presents the iterative learning control for the industrial robot manipulators including actuator dynamics. Motivated by human learning, the basic idea of iterative learning control is to use information from previous execution of a trial in order to improve performance from trial to trial. This is an advantage, when accurate model of the system is not available as friction and actuat...

This note deals with the problem of designing an iterative learning control for uncertain systems. Given a linear time-invariant plant with multiplicative perturbations, a suucient condition for the convergence of the learning process for all the plant uncertainty is derived. Based on the obtained suucient condition, we show that the iterative learning control design can be reformulated as the ...

An approach to the problem of propositional inductive learning of if-then-else rules, diierent from the commonly used ones, is presented in the paper. Main diierences are: literal selection process that searches for the smallest set of literals so that the completely correct rule for all learning examples can be constructed and the nonnormal form of the generated rules built by the search for n...

Strains are applied to the integration procedure in nonlinear increments todecrease the errors arising from the linearization of plastic equations. Two deformationvectors are used to achieve this. The first vector is based on the deformations obtained bythe first iteration of the equilibrium step, and the second is acquired from the sum of thesucceeding iterations. By applying these vectors and...