Extreme learning machine (ELM) as a new learning algorithm has been proposed for single-hidden layer feed-forward neural networks, ELM can overcome many drawbacks in the traditional gradient-based learning algorithm such as local minimal, improper learning rate, and low learning speed by randomly selecting input weights and hidden layer bias. However, ELM suffers from instability and over-fitti...

Generalized Extreme Learning Machine (GELM) is a kind of fast and efficient learning algorithm for training Generalized Single-layer hidden Feedforward Networks (GSLFNs) acting on some metric spaces. However, noisy data often produce over-fitting phenomena in practical applications. Therefore, an improved learning algorithm, called Regularized Generalized Extreme Learning Machine (R-GELM), is p...

Recent advances of high performance computing systems permits extreme scale computational simulation with high resolution numerical models. For uncertainty quantification of such extreme scale simulations, the computational cost of the traditional Monte Carlo simulations (MCS) may become extremely high. As an efficient alternative to MCS, the intrusive spectral stochastic finite element method ...

Given an assignment market, we introduce a set of vectors, one for each possible ordering on the player set, which we name the maxpayoff vectors. Each one of these vectors is obtained recursively only making use of the assignment matrix. Those max-payoff vectors that are efficient turn up to give the extreme core allocations of the market. When the assignment market has large core (that is to s...

In extreme value statistics, the extreme value index is a well-known parameter to measure the tail heaviness of a distribution. Pareto-type distributions, with strictly positive extreme value index (or tail index) are considered. The most prominent extreme value methods are constructed on efficient maximum likelihood estimators based on specific parametric models which are fitted to excesses ov...

The complicated manufacturing processes dictate that process variations are unavoidable in today's VLSI products. Unlike device variations, which can be captured by worst/best case corner points, the effects of interconnect variations are context-dependent, which makes it difficult to capture the true worst-case timing performance. This paper discusses an efficient method to explore the extreme...

Extreme learning machines are randomly initialized single-hidden layer feedforward neural networks where the training is restricted to the output weights in order to achieve fast learning with good performance. This contribution shows how batch intrinsic plasticity, a novel and efficient scheme for input specific tuning of non-linear transfer functions, and ridge regression can be combined to o...

Software systems analysis is a field in which analysts continually learn new techniques and approaches to properly capture, maintain, understand, and develop more efficient and effective software systems. We begin this research area overview by defining systems, systems analysis, and modeling. In subsequent sections, we focus on data and behavior representation of the system under study, protot...