Vertex cover and Edge vertex domination in trees

Let G = (V,E) be a simple graph. An edge e ∈ E(G) edge-vertex dominates a vertex v ∈ V (G) if e is incident with v or e is incident with a vertex adjacent to v. A subset D ⊆ E(G) is an edge-vertex dominating set of a graph G if every vertex of G is edge-vertex dominated by an edge of D. A vertex cover of G is a set C ⊆ V such that for each edge uv ∈ E at least one of u and v is in C. We characterize trees with edge-vertex domination number equals vertex covering number.

On the primary coverings of finite solvable and symmetric groups

A primary covering of a finite group 𝐺 is a family of proper subgroups of 𝐺 whose union contains the set of elements of 𝐺 having order a prime power. We denote by σ 0 ⁢ ( G ) \sigma_{0}(G) the smallest size of a primary covering of 𝐺 and call it the primary covering number of 𝐺. We study this number and compare it with its analogue σ ⁢ ( G ) \sigma(G) , the covering number, for the classes of groups 𝐺 that are solvable and symmetric.

Connected and Total Vertex covering in Graphs

A Subset S of vertices of a Graph G is called a vertex cover if S includes at least one end point of every edge of the Graph. A Vertex cover S of G is a connected vertex cover if the induced subgraph of S is connected. The minimum cardinality of such a set is called the connected vertex covering number and it is denoted by . A Vertex cover S of G is a total vertex cover if the induced subgraph of S has no isolates. The minimum cardinality of such a set is called the total vertex covering number and it is denoted by .In this paper a few properties of connected vertex cover and total vertex covers are studied and specific values of and of some well-known graphs are evaluated.

Universal consistency of twin support vector machines

A classification problem aims at constructing a best classifier with the smallest risk. When the sample size approaches infinity, the learning algorithms for a classification problem are characterized by an asymptotical property, i.e., universal consistency. It plays a crucial role in measuring the construction of classification rules. A universal consistent algorithm ensures that the larger the sample size of the algorithm is, the more accurately the distribution of the samples could be reconstructed. Support vector machines (SVMs) are regarded as one of the most important models in binary classification problems. How to effectively extend SVMs to twin support vector machines (TWSVMs) so as to improve performance of classification has gained increasing interest in many research areas recently. Many variants for TWSVMs have been proposed and used in practice. Thus in this paper, we focus on the universal consistency of TWSVMs in a binary classification setting. We first give a general framework for TWSVM classifiers that unifies most of the variants of TWSVMs for binary classification problems. Based on it, we then investigate the universal consistency of TWSVMs. To do this, we give some useful definitions of risk, Bayes risk and universal consistency for TWSVMs. Theoretical results indicate that universal consistency is valid for various TWSVM classifiers under some certain conditions, including covering number, localized covering number and stability. For applications of our general framework, several variants of TWSVMs are considered.