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Related Concept Videos

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...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Linearization and Approximation01:26

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
Probability Distributions01:32

Probability Distributions

The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Related Experiment Videos

Probability-confidence-kernel-based localized multiple kernel learning with lp norm.

Yina Han1, Guizhong Liu

  • 1School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China. ynhan@mailst.xjtu.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 21, 2012
PubMed
Summary
This summary is machine-generated.

Localized multiple kernel learning (LMKL) is enhanced by a new probability confidence kernel (PCK). This approach improves sample-specific model generalization for better performance on unseen data.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Computer Vision
  • Pattern Recognition

Background:

  • Localized Multiple Kernel Learning (LMKL) effectively combines heterogeneous features for sample-specific discrimination.
  • Existing LMKL models face challenges in balancing sample-specific fitting with generalization to unseen data.
  • Diverse locality characterization requires addressing both local model learning and extension to new data.

Purpose of the Study:

  • To develop an integrative solution for LMKL that addresses sample-specific modeling and generalization.
  • To introduce a novel Probability Confidence Kernel (PCK) for enhanced locality characterization in LMKL.
  • To propose and evaluate a new PCK-LMKL framework for improved performance on diverse datasets.

Main Methods:

  • Proposed a Probability Confidence Kernel (PCK) measuring per-sample similarity via probabilistic class attributes.
  • Integrated PCK with spatial-similarity-based kernels for comprehensive locality characterization.
  • Developed a joint optimization strategy for PCK parameters and the final classifier within a support-vector-machine-based LMKL framework, allowing for arbitrary l(p)-norm constraints.

Main Results:

  • The proposed PCK-LMKL achieved state-of-the-art performance on benchmark machine learning datasets (UCI).
  • Demonstrated superior results on challenging computer vision datasets, including the 15-scene and Caltech-101 datasets.
  • The PCK effectively complements base kernels, leading to more reasonable locality characterization and improved generalization.

Conclusions:

  • The novel PCK-LMKL framework offers an effective solution for learning sample-specific local models and generalizing to unseen data.
  • PCK provides statistical meaning and facilitates extension to the whole input space, enhancing LMKL capabilities.
  • The proposed method represents a significant advancement in localized multiple kernel learning, achieving top-tier performance across various domains.