Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Structures of Solids02:22

Structures of Solids

Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
The Seven Crystal Systems: Overview01:24

The Seven Crystal Systems: Overview

Crystals with various point group symmetries belong to different crystal classes, which are synonymous terms. Despite being in the same class, crystals may have distinct shapes, like cubes and octahedra. There are 32 three-dimensional point groups, all of which are systematically divided into seven crystal systems.The basic cubic crystal system, exemplified by NaCl, features orthogonal vectors (α = β = �� = 90°) of equal lengths (a = b = c). When specific requirements are not imposed on the...
Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Indeterminate Structure01:18

Indeterminate Structure

Indeterminate structures refer to structures where internal forces and reactions cannot be determined using only the equations of static equilibrium.  Indeterminate structures have more unknown forces and reaction forces than equations of static equilibrium that can be used to determine them. Indeterminate structures are often used in engineering to create complex, efficient, and aesthetically pleasing structures. There are various types of indeterminate structures used in engineering and some...

You might also read

Related Articles

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

Sort by
Same author

From code to care in hours: Why "go fast and fix things" is the new medical imperative.

Journal of experimental orthopaedics·2026
Same author

Preservation of preoperative CPAK is not associated with improved clinical outcomes one-year after functionally aligned robot-assisted TKA.

Journal of orthopaedics·2026
Same author

Restoring the native knee or designing the 'optimal prosthetic': Alignment, phenotypes and AI-powered personalization in total knee arthroplasty.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA·2025
Same author

Functional alignment improves femoral joint line obliquity preservation in comparison with the classical measured resection technique.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA·2025
Same author

Translation and cultural adaptation into spanish of the IKDC-Subjective knee form and Tegner Activity Scale.

Cirugia y cirujanos·2025
Same author

Cancer-associated venous thromboembolism and its impact on survival in the ONCOTHROMB12-01 cohort study.

International journal of cardiology. Heart & vasculature·2025

Related Experiment Video

Updated: Jun 23, 2026

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum
04:32

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Published on: March 19, 2017

Geometry-based ensembles: toward a structural characterization of the classification boundary.

Oriol Pujol1, David Masip

  • 1Departament de Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Edifici Històric, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain. oriol_pujol@ub.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 18, 2009
PubMed
Summary

This study presents a new binary classification method that approximates nonlinear boundaries using piecewise linear models. The approach yields a simple, robust classifier applicable to various machine learning challenges.

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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

Related Experiment Videos

Last Updated: Jun 23, 2026

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum
04:32

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Published on: March 19, 2017

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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

Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Computer Vision

Background:

  • Nonlinear decision boundaries pose challenges in binary classification.
  • Approximating complex decision boundaries requires robust methods.

Purpose of the Study:

  • Introduce a novel binary discriminative learning technique.
  • Develop a simple, robust, and geometrically meaningful classifier.
  • Extend the method for online, large-scale, and parallel learning.

Main Methods:

  • Approximation of nonlinear decision boundaries using a piecewise linear smooth additive model.
  • Geometric definition of decision boundaries via characterizing boundary points.
  • Assembly of locally robust linear classifiers using Tikhonov regularized optimization.

Main Results:

  • Obtained a simple, robust classifier with nonlinear behavior and strong geometrical interpretation.
  • Demonstrated linear computational complexity for online, large-scale, and parallel learning.
  • Validated performance on the UCI database against state-of-the-art techniques.

Conclusions:

  • The proposed method offers a promising approach for binary classification.
  • The technique's simplicity facilitates extensions to address modern machine learning challenges.
  • Successful application in diverse real-world problems including computer vision and pattern recognition.