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

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...

Classification of Systems-I

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:

Classification of Systems-II

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,

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Clinical Utility of Postoperative Day 1 Technetium-99m Mercaptoacetyltriglycine Scintigraphy for Early Assessment of Graft Function in Living Donor Kidney Transplant Recipients.

Journal of transplantation·2026

Same author

Structural insights into the interaction between the BH3-like domain of hepatitis B virus X protein and LC3B.

Biochimica et biophysica acta. Proteins and proteomics·2026

Same author

Computed Tomography Findings of Pulmonary Lymphoma in a Dog and Two Cats.

Veterinary medicine and science·2026

Same author

Conservative management of cranial cruciate ligament injury in an Asian small-clawed otter (Aonyx cinereus).

The Journal of veterinary medical science·2026

Same author

Post-bronchoscopy sputum culture improves detection of nontuberculous mycobacterial pulmonary disease: A retrospective Study.

Respiratory investigation·2026

Same author

Lung adenocarcinoma with <i>ALK</i> deletion of exons 6-20 with response to sequential ALK inhibitors: a case report.

Translational lung cancer research·2025

Search research articles

Related Experiment Video

Updated: Jun 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Complexities of feature-based learning systems, with application to reservoir computing.

Hiroki Yasumoto¹, Toshiyuki Tanaka¹

¹Graduate School of Informatics, Kyoto University, 36-1, Yoshida Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.

Neural Networks : the Official Journal of the International Neural Network Society

|November 16, 2024

Summary

This study explores complexity measures for feature-based learning systems, including reservoir systems. It analyzes how system properties like unadjustability and linearity impact complexity, offering improved theoretical insights.

Keywords:

Growth function Pseudo-dimension Rademacher complexity Reservoir computing VC-dimension

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Related Experiment Videos

Last Updated: Jun 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Machine Learning
Computational Complexity Theory

Background:

Reservoir computing systems are complex models used in machine learning.
Understanding the complexity of these systems is crucial for their effective application.
Existing complexity measures may not fully capture the nuances of reservoir systems.

Purpose of the Study:

To introduce and analyze a generalized feature-based learning system model.
To investigate various complexity measures, including growth function, VC-dimension, pseudo-dimension, and Rademacher complexity, within this framework.
To examine the influence of reservoir system characteristics on these complexity measures.

Main Methods:

Development of a generalized feature-based learning system model.
Application and analysis of established complexity measures (growth function, VC-dimension, pseudo-dimension, Rademacher complexity).
Theoretical analysis of the impact of unadjustability and linearity in reservoir systems on complexity.

Main Results:

The study provides a generalized framework for analyzing learning system complexity.
Specific impacts of reservoir unadjustability and readout linearity on complexity measures are elucidated.
New theoretical results are presented, generalizing and improving upon existing findings in the field.

Conclusions:

The generalized model offers a more comprehensive approach to understanding learning system complexity.
The findings provide valuable insights into designing and optimizing reservoir computing systems.
This work contributes to the theoretical foundations of machine learning and computational complexity.