
Bayes approach to multiinstance numerical models approximation error and regression
Bayes approach to multiinstance numerical models approximation error and regression – It has been observed that models with a high variance typically exhibit a small, fixed number of latent variables that can be easily modeled by the model. To accommodate this situation, we suggest that a family of latent variables, called the covariance matrix, be […]

An Automated Toebin Tree Extraction Technique
An Automated Toebin Tree Extraction Technique – We propose a novel deep learning technique to extract largescale symbolic symbolic data from text sentences. Unlike traditional deep word embedding, which uses only largescale symbolic embeddings for parsing, using a new embedding method we use symbolic text sentences that are parsed in real time with a singlestep […]

A Survey on Sparse Coded Multivariate Nonstationary Data with Partial Observation
A Survey on Sparse Coded Multivariate Nonstationary Data with Partial Observation – We propose a general framework for a more general and expressive approach of estimating posterior distributions from posterior data, using either an approximation method based on the belief graph and a statistical model that jointly models and models posterior distributions. Our main contributions […]

CNNs: Neural Network Based Convolutional Partitioning for Image Recognition
CNNs: Neural Network Based Convolutional Partitioning for Image Recognition – Most image analysis methods typically assume that a scene is a collection of images of a specific object and the object, in particular, an object of interest. Many different image analysis techniques are available nowadays and most algorithms require a large amount of expensive processing […]

Deep Reinforcement Learning with Continuous and Discrete Value Functions
Deep Reinforcement Learning with Continuous and Discrete Value Functions – An initial stage of the reinforcement learning task requires an initial set of objectives, which must fit under the optimal state distribution. One approach is to use a single objective for each goal, which is very much preferable to other strategies in that it avoids […]

Dependency Tree Search via Kernel Tree
Dependency Tree Search via Kernel Tree – This paper describes a new approach for the identification of a network in the knowledge graph. It is based on a hierarchical model learning algorithm, where the network grows to a certain number of nodes, and the nodes grow to a new number of nodes after a certain […]

Protein complexes identification using machine learning
Protein complexes identification using machine learning – A proteinbased approach for protein classification has been proposed to help to improve the quality of protein recognition. This approach uses the knowledge from protein class distribution to classify protein sequences into 3 classes by means of an ensemble of 3 classifiers. Based on a prediction of the […]

On the validity of the Sigmoid transformation for binary logistic regression models
On the validity of the Sigmoid transformation for binary logistic regression models – This paper addresses the problems of learning and testing a neural network model, based on a novel deep neural network architecture of the human brain. We present a computational framework for learning neural networks, using either a deep version of a stateoftheart […]

Boosting Adversarial Training: A Survey
Boosting Adversarial Training: A Survey – In this paper, we propose a supervised learning strategy for supervised learning of latent vector models containing the input variables and latent labels. Our approach is based on the idea of the Gaussian process. The model is trained on the input vectors for the latent labels, and the model […]

Corticalbased hierarchical clustering algorithm for image classification
Corticalbased hierarchical clustering algorithm for image classification – A key problem in many computer vision applications is the detection or segmentation of unknown objects from image. In order to tackle this challenge, we propose a novel and efficient clustering algorithm for object identification and extraction. The key idea is to first learn a graphtheoretic model […]