Extracting bio-entity relations has emerged as an important task due to the ever-growing number of bio-medical documents. In this paper, we present a simple and novel representation for extracting bio-entity relationships. The state-of-theart systems for such tasks rely on word based representations and variations of linguistic driven features. In contrast, we model bio-text by the most basic c...

We present some variations on of the main open problems character degrees. collect methods that have proven to be very useful work these problems. These are also solve certain zeros characters, kernels and fields values characters.

The application of kernel-based learning algorithms has, so far, largely been confined to realvalued data and a few special data types, such as strings. In this paper we propose a general method of constructing natural families of kernels over discrete structures, based on the matrix exponentiation idea. In particular, we focus on generating kernels on graphs, for which we propose a special cla...

The application of kernel-based learning algorithms has, so far, largely been confined to realvalued data and a few special data types, such as strings. In this paper we propose a general method of constructing natural families of kernels over discrete structures, based on the matrix exponentiation idea. In particular, we focus on generating kernels on graphs, for which we propose a special cla...

Deep metric learning is one of the recommended methods for challenge supporting few/zero-shot by deep networks. It depends on building a Siamese architecture two homogeneous Convolutional Neural Networks (CNNs) distance function that can map input data from space to feature space. Instead determining class each sample, deals with existence few training samples deciding if share same identity or...

Over the last few years, two different notions of positive definite (pd) kernels—universal and characteristic—have been developing in parallel in machine learning: universal kernels are proposed in the context of achieving the Bayes risk by kernel-based classification/regression algorithms while characteristic kernels are introduced in the context of distinguishing probability measures by embed...

Recent advances in constant-time technology and atomic epistemologies do not necessarily obviate the need for thin clients. In fact, few information theorists would disagree with the unfortunate unification of Moore’s Law and flipflop gates, which embodies the private principles of cyberinformatics. We construct a homogeneous tool for analyzing kernels (TOTA), confirming that IPv4 and kernels a...