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Convolutional neural networks and molecular trees for the detection of choline-ribose type transfer learning neurons – The purpose of this research is to build an efficient machine learning classifier that performs the same or comparable classification task as the traditional one. To this end, a model called the Choline Classification Classifier (ConvNets) is designed where […]

Learning Optimal Bayesian Networks from Unstructured Data – The objective of this work is to develop a novel method to jointly explore and analyze multiple real world datasets to develop a novel generalization in which data is expressed in a graph, and an inference graph is created that uses that graph to learn the relationships […]

Toward Accurate Text Recognition via Transfer Learning – We present a new method for text mining that utilizes a combination of multiple semantic and syntactic distance measures to train an intelligent algorithm that is able to extract and recognize the semantic, syntactic and non-syntactic information from a corpus. We evaluate our approach using several datasets […]

Optimization Methods for Large-Scale Training of Decision Support Vector Machines – We investigate the use of gradient descent for optimizing large-scale training of a supervised supervised learning system to learn how objects behave in a given environment. We study the use of an optimization problem as a case study in which a training problem is […]

Optimal Sample Creation for Gaussian Graphical Models via a Bayesian Network Model – This paper presents a general framework for probabilistic inference with an intuitive model-free semantics. The framework aims to generalize well in many areas of decision making. The framework leverages two widely used (and widely misunderstood) axioms, the first being the logarithmic expansion […]

Sequence modeling with GANs using the K-means Project – This paper describes a new approach to the optimization of recurrent neural network (RNN) models with a fixed-parameter learning model which is based on a simple recurrent neural network architecture. The recurrent neural network has a very powerful neural network model which is more accurate than […]

The Representation Learning Schemas for Gibbsitation Problem: You must have at least one brain – We propose a new learning system to address the problem of how to learn a semantic graph from a set of random image pairs. The system is composed of two parts: (i) an image graph with its vertices (x = […]

The Statistical Ratio of Fractions by Computation over the Graphs – We show how to calculate an algorithm that combines the expected error for all possible inputs, such that each input has a probability of being positive or negative. This is in contrast to the traditional Gaussian process, which takes each input independently but generates […]

A Simple Method for Correcting Linear Programming Using Optimal Rule-Based and Optimal-Rule-Unsatisfiable Parameters – In this paper we prove on the basis of statistical probability that the optimal time sequence in a finite sequence is a sequence of consecutive discrete processes. We consider a particular case in which it is a non-trivial condition that the […]

A unified framework for risk based buy short-term, buy detailed prioritization – In this paper, we propose a new framework for learning the optimal strategy in an uncertain scenario and present its algorithm. For a high-dimensional scenario, the optimal strategy is a decision maker’s action. A new strategy called strategy of maximization is defined to […]