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

Machines

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...

Machines: Problem Solving II

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

Machines: Problem Solving I

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

You might also read

Related Articles

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

Sort by

Same author

Widespread use of invalid statistical tests in biomedical machine learning.

bioRxiv : the preprint server for biology·2026

Same author

Danilo Bzdok.

Neuron·2026

Same author

Cell type transcriptomic modules reveal shared molecular mechanisms in Alzheimer's and Parkinson's disease.

GigaScience·2026

Same author

Double robustness.

Nature methods·2026

Same author

An international mega-analysis of psychedelic drug effects on brain circuit function.

Nature medicine·2026

Same author

Threading the Needle: Practical Considerations for Merging Theory-Driven Computational Psychiatry With Data-Driven Analytics to Enhance Precision Health at Scale.

Biological psychiatry. Cognitive neuroscience and neuroimaging·2026

Same journal

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.

Nature methods·2026

Same journal

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Nature methods·2026

Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methods·2026

Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026

Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methods·2026

Same journal

Deep molecular profiling in three dimensions.

Nature methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 6, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Machine learning: supervised methods.

Danilo Bzdok¹, Martin Krzywinski², Naomi Altman³

¹Department of Psychiatry, RWTH Aachen University, in Germany and a Visiting Professor at INRIA/Neurospin Saclay in France.

|August 14, 2018

Summary

This summary is machine-generated.

Supervised learning uses example data to teach algorithms general rules for making predictions. This approach is key for developing effective machine learning models.

More Related Videos

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Feb 6, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Supervised learning is a fundamental machine learning paradigm.
It involves learning a function that maps inputs to outputs based on example input-output pairs.

Purpose of the Study:

To elucidate the core mechanism of supervised learning algorithms.
To highlight the role of prediction objectives in guiding the learning process.

Main Methods:

Algorithms analyze datasets containing labeled examples.
General principles are extracted through iterative refinement based on prediction accuracy.

Main Results:

Supervised learning successfully identifies patterns and relationships within data.
The process yields models capable of making accurate predictions on new, unseen data.

Conclusions:

Supervised learning provides a robust framework for predictive modeling.
Its effectiveness hinges on the quality of training data and the clarity of the prediction objective.