Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Chlorocholine chloride disrupts differentiation of rat stem/progenitor Leydig cells: A combined in vivo and in vitro study by Leydig cell regeneration model.

Environmental toxicology and pharmacology·2026

Same author

Ion-specific regulation of epoxy conversion and hydroxyl migration on graphene oxide.

Physical chemistry chemical physics : PCCP·2026

Same author

Effects of temperature changes on oxidative stress and mud crab reovirus (MCRV) replication in mud crab (Scylla paramamosain).

Fish & shellfish immunology·2026

Same author

Palliative radiotherapy plus multimodal analgesia for painful bone metastases: improved pain control and survival.

American journal of cancer research·2026

Same author

Mineral-Derived Nanozymes Modulate Plant Redox Homeostasis and Nitrogen Metabolism to Improve Crop Productivity.

ACS nano·2026

Same author

Synergistic Roles of InlA, InlB and LLO in the Infection of Trigeminal Ganglion Neurons by Ovine-Derived <i>Listeria monocytogenes</i> LM90SB2.

Animals : an open access journal from MDPI·2026

Search research articles

Related Experiment Video

Updated: Nov 27, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

DBN Structure Design Algorithm for Different Datasets Based on Information Entropy and Reconstruction Error.

Jianjun Jiang¹, Jing Zhang¹, Lijia Zhang¹

¹National Digital Switching System Engineering and Technological Research Center (NDSC), Zhengzhou 450000, Henan, China.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

This summary is machine-generated.

A new algorithm optimizes deep belief network (DBN) structure using information entropy and reconstruction error. This method simultaneously designs network depth and nodes, improving performance and reducing errors for diverse datasets.

Keywords:

DBN artificial intelligence deep learning improved simulated annealing algorithm information entropy reconstruction error structure design

More Related Videos

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Nov 27, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Artificial Intelligence
Machine Learning
Deep Learning

Background:

Deep Belief Networks (DBNs) are powerful deep learning models with broad applications.
Designing optimal DBN structures for specific datasets remains a significant challenge.

Purpose of the Study:

To propose a novel algorithm for automated DBN structure design.
To simultaneously optimize network depth and the number of nodes.

Main Methods:

Developed a mathematical model for DBN structural design.
Incorporated information entropy for node number constraints and reconstruction error for performance optimization.
Utilized an improved simulated annealing (ISA) algorithm for simultaneous layer and node adjustment.

Main Results:

The proposed algorithm successfully designed appropriate DBN structures for MNIST, Cifar-10, and Cifar-100 datasets.
Achieved the lowest reconstruction error and prediction error rates compared to other methods.
Demonstrated superior performance in assisting DBN parameter settings.

Conclusions:

The novel DBN structure design algorithm effectively adapts to different datasets.
This approach offers a robust solution for optimizing DBN architectures, leading to improved predictive accuracy.