This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was...

Aki Vehtari and Jouko Lampinen Laboratory of Computational Engineering, Helsinki University of Te hnology P.O.Box 9400, FIN-02015 HUT, FINLAND SMCia/99 1999 IEEE Midnight-Sun Workshop on Soft Computing Methods in Industrial Appli ations Kuusamo, Finland, June 16 18, 1999 Abstra t We demonstrate the advantages of using Bayesian neural networks in regression, inverse and lassi ation problems, whi...

The conformational energy values of 3-amino-4-nitraminofurazan (C2N4O3H2) molecule changing with two torsion angles were firstly calculated using density functional theory (DFT) with Lee-Young-Parr correlation functional and 6-31 G(d) basis set on Gaussian Program. And then, these obtained discrete data were made continuous by using Fuzzy Logic Modelling (FLM) and Artificial Neural Network (ANN...

Breast Cancer is one of the fatal diseases causing more number of deaths in women. Constant efforts are being made to develop more efficient techniques for early and accurate diagnosis of breast cancer. Classical methods required cytopathologists or oncologists to examine the breast lesions for detection and classification of various stages of the cancer. Such manual attempts have proven to be ...

We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. We also discuss its Bayesian interpretation and justiication. We rst review previous results for the approximation of a function from discrete data (Girosi, 1998) in the co...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

Evidence of the strong relationship between learning and emotion has fueled recent work in modeling affective states in intelligent tutoring systems. Many of these models are based on general models of affect without a specific focus on learner emotions. This paper presents work that investigates the benefits of using theoretical models of learner emotions to guide the development of Bayesian n...

In this study we propose a multilayered feedforward neural network (MFNN) with Backpropagation Learning Rule Incorporating Bayesian Regularization, and apply it to the credit risk evaluation problem domain using a real world data set from a financial services company in England. We choose the MFNN because of its broad applicability to many problem domains of relevance to business: principally p...

We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equivalence provides a new understanding of kernel Bayesian inference. Moreover, the optimization problem induces a new regularization for the posterior embedding estimator, which is faster and has comparable performance...