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Related Concept Videos

Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Related Experiment Video

Updated: May 21, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A new approach to a maximum à posteriori-based kernel classification method.

Nopriadi1, Yukihiko Yamashita

  • 1Department of International Development Engineering, Graduate School of Science and Engineering, Tokyo Institute of Technology, South 6th Building, Ookayama, Meguro-ku, Tokyo 152-8552, Japan. nopriadi@yahoo.com

Neural Networks : the Official Journal of the International Neural Network Society
|June 23, 2012
PubMed
Summary

A new method called MAP-based kernel classification trained by linear programming (MAPLP) offers a novel approach to classification. This method shows promising performance compared to existing techniques, simplifying objective functions for future research.

Related Experiment Videos

Last Updated: May 21, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Machine Learning
  • Computer Science
  • Statistical Classification

Background:

  • Traditional Maximum A Posteriori (MAP)-based classifiers often require direct estimation of posterior probabilities.
  • Existing kernel classification methods can be complex and constrained by strict Bayesian assumptions.

Purpose of the Study:

  • To introduce a novel Maximum A Posteriori (MAP)-based kernel classification approach trained by linear programming (MAPLP).
  • To evaluate the performance of MAPLP against conventional methods and state-of-the-art classifiers.
  • To explore the flexibility and simplicity of the proposed objective function for future research.

Main Methods:

  • Developed MAP-based kernel classification trained by linear programming (MAPLP).
  • Introduced a kernelized function into an objective function, bypassing direct posterior probability estimation.
  • Conducted binary classification experiments on 13 diverse datasets.

Main Results:

  • MAPLP demonstrated promising performance in binary classification tasks.
  • The proposed method achieved competitive results when compared to conventional MAP-based kernel classifiers and other state-of-the-art techniques.
  • MAPLP offers greater flexibility in choosing objective functions and is not strictly constrained by Bayesian principles.

Conclusions:

  • MAPLP represents a significant contribution to MAP-based classification research.
  • The approach allows for a simpler, single-parameter objective function, facilitating future development.
  • MAPLP's ability to be solved via linear programming enhances its practical applicability.