Covariance matrices are important in many areas of neural modelling. In Hop eld networks they are used to form the weight matrix which controls the autoassociative properties of the network. In Gaussian processes, which have been shown to be the in nite neuron limit of many regularised feedforward neural networks, covariance matrices control the form of Bayesian prior distribution over function...

In this work we propose the hybridization of two techniques to improve the cooperation among the fuzzy rules: the use of rule weights and the Cooperative Rules learning methodology. To do that, the said methodology is extended to include the learning of rule weights within the rule cooperation paradigm. Considering these kinds of techniques could result in important improvements of the system a...

1&2 Helsinki University of Technology, Dept of Computer Science and Eng., 1 Software Business and Engineering Institute, P.O.B. 9600, FI-02015 HUT 2 Lab. for Theoretical Computer Science, P.O.Box 5400, FI-02015 HUT 1&2 {Juha.Tiihonen, Timo.Soininen, Ilkka.Niemela, Reijo.Sulonen}@hut.fi Abstract. In this paper we first describe a configurator implementation based on a practically important subse...

We derive and analyze the f-sum rule for a two-dimensional (2D) system of interacting electrons whose behavior is described by the Dirac equation. We apply the sum rule to analyze the spectral weight transfer in graphene within different approximations discussed in the literature. We find that the sum rule is generically dominated by inter-band transitions while other excitations produce sub-le...

In this paper, we propose a simple and efficient method to construct an accurate fuzzy classification system. In order to optimize the generalization accuracy, we use ruleweight as a simple mechanism to tune the classifier and propose a new learning method to iteratively adjust the weight of fuzzy rules. The rule-weights in the proposed method are derived by solving the minimization problem thr...

We consider a category of gl∞-crystals, whose object is a disjoint union of extremal weight crystals with bounded non-negative level and finite multiplicity for each connected component. We show that it is a monoidal category under tensor product of crystals and the associated Grothendieck ring is antiisomorphic to an Ore extension of the character ring of integrable lowest gl∞modules with resp...

The paper reviews single-neuron learning rules for minor component analysis and suggests a novel minor component learning rule. In this rule, the weight vector length is self-stabilizing, i.e., moving towards unit length in each learning step. In simulations with low- and medium-dimensional data, the performance of the novel learning rule is compared with previously suggested rules.

Weighted association rule mining reflects semantic significance of item by considering its weight. Classification constructs the classifier and predicts the new data instance. This paper proposes compact weighted class association rule mining method, which applies weighted association rule mining in the classification and constructs an efficient weighted associative classifier. This proposed as...

This paper shows that monetary policy should be delegated to a central bank that cross-checks optimal policy with information from the Taylor rule. Attaching some weight to deviations of the interest rate from the interest rate prescribed by the Taylor rule is beneficial if the central bank aims at optimally stabilizing inflation and output gap variability under discretion. Placing a weight on ...