Cheng Wu

TL;DR: It is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework, which provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory.

TL;DR: A novel feature learning method for domain adaptation to construct both domain invariant and class discriminative representations, referred to as DICD, which reduces the domain difference by jointly matching the marginal and class-conditional distributions of both domains, and simultaneously maximizes the inter-class dispersion and minimizes the intra-class scatter.

TL;DR: Numerical computations show that the energy-saving module of the extended NEH-Insertion Procedure in MONEH and MMOIG significantly helps to improve the discovered front and the proposed algorithms perform more effectively than other tested high-performing meta-heurisitics in searching for non-dominated solutions.

TL;DR: This paper studies the unrelated parallel machine scheduling problem under a TOU pricing scheme and reformulates the problem using Dantzig-Wolfe decomposition and proposes a column generation heuristic to solve it.

TL;DR: This paper proposes two kinds of reduction methods for the reduction of the concept lattices based on rough set theory and presents the sufficient and necessary conditions for justifying whether an attribute and an object are dispensable or indispensable in the above concept lattice.