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Related Concept Videos

Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Resonance and Hybrid Structures02:16

Resonance and Hybrid Structures

According to the theory of resonance, if two or more Lewis structures with the same arrangement of atoms can be written for a molecule, ion, or radical, the actual distribution of electrons is an average of that shown by the various Lewis structures.
Resonance Structures and Resonance Hybrids
The Lewis structure of a nitrite anion (NO2−) may actually be drawn in two different ways, distinguished by the locations of the N–O and N=O bonds.
Structure of Benzene: Molecular Orbital Model01:18

Structure of Benzene: Molecular Orbital Model

According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
Structural Classification of Joints01:20

Structural Classification of Joints

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Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

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Related Experiment Video

Updated: Jun 27, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

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

Published on: February 15, 2017

BYY harmony learning, structural RPCL, and topological self-organizing on mixture models.

Lei Xu1

  • 1Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, NT, People's Republic of China. lxu@cse.cuhk.edu.hk

Neural Networks : the Official Journal of the International Neural Network Society
|November 6, 2002
PubMed
Summary

Bayesian Ying-Yang (BYY) harmony learning advances unsupervised and supervised mixture models using adaptive algorithms and new criteria for automatic model selection. This framework enhances clustering, mixture-of-experts, and topological maps with improved statistical learning.

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Area of Science:

  • Statistical Learning
  • Machine Learning
  • Artificial Intelligence

Background:

  • The Bayesian Ying-Yang (BYY) harmony learning framework offers novel regularization and model selection mechanisms.
  • Existing methods require distinct parameter learning and model selection phases.

Purpose of the Study:

  • To present further advances on BYY harmony learning by incorporating modular inner representations.
  • To detail adaptive learning algorithms and model selection criteria within the BYY framework.

Main Methods:

  • Developed unsupervised mixture models (Gaussian, elliptic, subspace, Non-Gaussian) with BYY harmony learning.
  • Applied BYY harmony learning to supervised mixture-of-experts (ME) models (Gaussian ME, RBF nets, kernel regression).
  • Extended structural mixtures into self-organized topological maps using BYY principles.

Main Results:

  • Introduced adaptive learning algorithms (elliptic, subspace, structural rival penalized competitive learning) with automatic model selection.
  • Defined new model selection criteria for post-parameter learning evaluation.
  • Demonstrated the derivation of these algorithms and criteria from BYY harmony learning special cases.

Conclusions:

  • The presented advances extend the capabilities of BYY harmony learning for diverse statistical modeling tasks.
  • The integrated approach of adaptive learning and model selection simplifies and improves the process.
  • This work provides a unified perspective on advanced statistical learning through the BYY harmony learning framework.