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Found 17 papers, 0 papers with code
An Embedded System for Image-based Crack Detection by using Fine-Tuning model of Adaptive Structural Learning of Deep Belief Network
In our research, an adaptive structural learning method of Restricted Boltzmann Machine (Adaptive RBM) and Deep Belief Network (Adaptive DBN) have been developed as a deep learning model.
Automatic Extraction of Road Networks from Satellite Images by using Adaptive Structural Deep Belief Network
Our Adaptive DBN had an advantage of not only the detection accuracy but also the inference time compared with the conventional CNN in the experiment results.
A Distillation Learning Model of Adaptive Structural Deep Belief Network for AffectNet: Facial Expression Image Database
In order to represent such cases, this paper investigated a distillation learning model of Adaptive DBN.
An Adaptive Structural Learning of Deep Belief Network for Image-based Crack Detection in Concrete Structures Using SDNET2018
The dataset contains about 56, 000 crack images for three types of concrete structures: bridge decks, walls, and paved roads.
Re-learning of Child Model for Misclassified data by using KL Divergence in AffectNet: A Database for Facial Expression
In order to distinguish such cases, this paper investigated a re-learning model of Adaptive DBN with two or more child models, where the original trained model can be seen as a parent model and then new child models are generated for some misclassified cases.
An Object Detection by using Adaptive Structural Learning of Deep Belief Network
In this paper, a new object detection method for the DBN architecture is proposed for localization and category of objects.
A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network
The method can find the optimal number of hidden neurons of a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm to train the given input data, and then it can make a new layer in DBN by the layer generation algorithm to actualize a deep data representation.
Adaptive Structural Learning of Deep Belief Network for Medical Examination Data and Its Knowledge Extraction by using C4.5
The adaptive structural learning method of Deep Belief Network (DBN) has been developed.
Knowledge Extracted from Recurrent Deep Belief Network for Real Time Deterministic Control
We can success the knowledge extraction from the trained deep learning with high classification capability.
Adaptive Learning Method of Recurrent Temporal Deep Belief Network to Analyze Time Series Data
Deep Learning has the hierarchical network architecture to represent the complicated features of input patterns.
Shortening Time Required for Adaptive Structural Learning Method of Deep Belief Network with Multi-Modal Data Arrangement
For this reason, not only the numerical data and text data but also the time-series data are transformed to the image data format.
An Adaptive Learning Method of Deep Belief Network by Layer Generation Algorithm
In this paper, we propose the adaptive learning method of DBN that can determine the optimal number of layers during the learning.
An Adaptive Learning Method of Restricted Boltzmann Machine by Neuron Generation and Annihilation Algorithm
In this method, a new hidden neuron is generated if the energy function is not still converged and the variance of the parameters is large.
Fine Tuning Method by using Knowledge Acquisition from Deep Belief Network
As a result, the classification capability can achieve a great success (97. 1\% to unknown data set).
A Generation Method of Immunological Memory in Clonal Selection Algorithm by using Restricted Boltzmann Machines
However, some landmarks was not detected correctly by the previous method because they didn't have enough amount of information for the feature extraction.
Clustering and Retrieval Method of Immunological Memory Cell in Clonal Selection Algorithm
The clonal selection principle explains the basic features of an adaptive immune response to a antigenic stimulus.
A Clonal Selection Algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM
Mobile Phone based Participatory Sensing (MPPS) system involves a community of users sending personal information and participating in autonomous sensing through their mobile phones.
