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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Residuals and Least-Squares Property

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...

Clearance Models: Noncompartmental Models

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...

Aggregates Classification

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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

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

Sort by

Same author

Data Fusion for Partial Identification of Causal Effects.

Advances in neural information processing systems·2026

Same author

A Double Machine Learning Approach for Combining Experimental and Observational Studies.

Observational studies·2026

Same author

Metabolic engineering of Saccharomyces cerevisiae for efficient production of dihydroartemisinic acid.

Bioresource technology·2026

Same author

Data-hugging shields proprietary AI models from research that could disprove them.

NPJ artificial intelligence·2026

Same author

Coordination Optimization of Co Active Sites by Sb-Induced Phase Transition for Enhanced Electrochemical Propylene Epoxidation at Low Cl<sup>-</sup> Concentration Conditions.

Angewandte Chemie (International ed. in English)·2026

Same author

Second Volume of the Special Issue on: "Artificial Intelligence for Risk Analysis and the Risks of AI".

Risk analysis : an official publication of the Society for Risk Analysis·2026

Same journal

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift.

Proceedings of machine learning research·2026

Same journal

Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video.

Proceedings of machine learning research·2026

Same journal

Perspective: Machine Learning for Health Should Consider Social Drivers of Health.

Proceedings of machine learning research·2026

Same journal

Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression.

Proceedings of machine learning research·2026

Same journal

Does Domain-Specific Retrieval Augmented Generation Help LLMs Answer Consumer Health Questions?

Proceedings of machine learning research·2026

Same journal

Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential.

Proceedings of machine learning research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 22, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Fast Sparse Classification for Generalized Linear and Additive Models.

Jiachang Liu¹, Chudi Zhong¹, Margo Seltzer²

¹Duke University.

Proceedings of Machine Learning Research

|May 23, 2022

Summary

This summary is machine-generated.

We developed fast classification methods for sparse generalized linear and additive models, enabling efficient analysis of large datasets with thousands of features and observations. These techniques offer speed-ups and interpretable models with comparable accuracy to complex methods.

More Related Videos

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Related Experiment Videos

Last Updated: Sep 22, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Area of Science:

Machine Learning
Statistical Modeling
Computational Science

Background:

Sparse generalized linear and additive models are crucial for analyzing high-dimensional data.
Existing classification techniques can be computationally intensive, especially with large datasets and correlated features.
There is a need for faster, yet accurate, classification methods that produce interpretable models.

Purpose of the Study:

To present novel, fast classification techniques for sparse generalized linear and additive models.
To improve computational efficiency for models with thousands of features and observations.
To develop methods that yield interpretable models with accuracy comparable to black-box approaches.

Main Methods:

Developed fast classification techniques utilizing linear and quadratic surrogate cuts for logistic loss to efficiently screen features.
Employed a priority queue for more uniform feature exploration in sparse logistic regression.
Proposed an exponential loss function allowing for an analytical solution during line search iterations.
Algorithms designed to handle thousands of features and observations, including highly correlated ones.

Main Results:

Achieved computational speed-ups of 2 to 5 times compared to previous best-subset search techniques.
Demonstrated ability to handle datasets with thousands of features and observations in minutes.
Generated interpretable models with accuracy comparable to black-box models on challenging datasets.
Successfully screened features for elimination efficiently using surrogate cuts.

Conclusions:

The presented fast classification techniques significantly enhance the efficiency of analyzing sparse generalized linear and additive models.
These methods provide a viable alternative for building accurate and interpretable models from large, complex datasets.
The use of surrogate cuts and exponential loss offers substantial computational advantages in feature selection and model training.