Gaussian process (GP) models form a new, emerging complementary method for nonlinear system identification. The GP model is a probabilistic nonparametric black-box model. It differs from most of the other frequently used black-box identification approaches as it does not try to approximate the modeled system by fitting the parameters of the selected basis functions, but rather searches for the ...

Profound changes are taking place in the world economy and in the innovation systems of countries. The main influencing trends could be summarized by the terms globalization, liberalization, dematerialization, and technological revolution. Their joint effects have been the enhanced uncertainty and turbulence felt in the world economic system since the 1973 crisis and the gradual emergence of a ...

Learning from Demonstration (LfD) is a paradigm that allows robots to learn complex manipulation tasks can not be easily scripted, but demonstrated by human teacher. One of the challenges LfD enable acquire skills adapted different scenarios. In this letter, we propose achieve exploiting variations in demonstrations retrieve an adaptive and robust policy, using Gaussian Process (GP) models. Ada...

Genetic programming (GP) offers a generic method of automatically fusing together classifiers using their receiver operating characteristics (ROC) to yield superior ensembles. We combine decision trees (C4.5) and artificial neural networks (ANN) on a difficult pharmaceutical data mining (KDD) drug discovery application. Specifically predicting inhibition of a P450 enzyme. Training data came fro...

We describe three computer searches (in PARI/GP, Maple, and Mathematica, respectively) which led to the discovery of a number of identities of Rogers-Ramanujan type and identities of false theta functions.

In the field of empirical modeling using Genetic Programming (GP), it is important to evolve solution with good generalization ability. Generalization ability of GP solutions get affected by two important issues: bloat and over-fitting. Bloat is uncontrolled growth of code without any gain in fitness and important issue in GP. We surveyed and classified existing literature related to different ...

Infrastructure for the automatic collection of single-point measurements of snow water equivalent (SWE) is well-established. However, because SWE varies significantly over space, the estimation of SWE at the catchment scale based on a single-point measurement is error-prone. We propose low-cost, lightweight methods for near-real-time estimation of mean catchment-wide SWE using existing infrastr...

The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the 'cost' of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision-theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via...

In recent years, digital reconstruction of cultural heritage provides an effective way of protecting historical relics, in which the modeling of surface reflection of historical heritage with high fidelity places a very important role. In this paper Gaussian process (GP) regression based approach is proposed to model the reflection properties of real materials, in which the simulation data gene...