Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Migration00:53

Migration

8.7K
Migration is long-range, seasonal movement from one region or habitat to another. This common strategy, carried out by many different organisms around the world, is an adaptive response that typically corresponds to changes in an organism’s environment, like resource availability or climate. Migrations can involve huge groups of thousands of animals as well as single individuals traveling alone and can range from thousands of kilometers to just a few hundred meters.
8.7K
Classification of Systems-I01:26

Classification of Systems-I

544
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:
544
Classification of Systems-II01:31

Classification of Systems-II

453
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,
453
Aggregates Classification01:29

Aggregates Classification

963
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...
963
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.3K
Observational Learning01:12

Observational Learning

817
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
817

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Investigation of thermal conductivity for nano-improved polyethylene glycol composites.

Discover nano·2026
Same author

Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.

Abdominal radiology (New York)·2025
Same author

Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics.

Scientific reports·2025
Same author

A blockchain secured metaverse framework for scalable and immersive telemedicine.

Scientific reports·2025
Same author

Low-dose computed tomography image denoising using pixel level non-local self-similarity prior with non-local means for healthcare informatics.

Scientific reports·2025
Same author

Enhancing education quality with hybrid clustering and evolutionary neural networks in a multi phase framework.

Scientific reports·2025

関連する実験動画

Updated: Jun 28, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

転移学習ベースの分類器の適応アンサンブルのための生物学的インスピレーションを受けた象の群れの最適化ベースの方法

Om Prakash Suthar1, Vijay Katkar2, Krunal Vaghela1

  • 1Department of Computer Engineering, Marwadi University, Rajkot, Gujarat 360003, India.

MethodsX
|January 6, 2026
PubMed
まとめ

この研究では、限られたデータで画像分類精度を向上させるために、象の群れの最適化（EHO）を使用した適応アンサンブル法を紹介します。この新しいアプローチは、GAITおよびODIR-5Kデータセットでより良いパフォーマンスのために分類器の選択を強化します。

キーワード:
転移学習アンサンブル学習象の群れの最適化画像分類適応学習

さらに関連する動画

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

関連する実験動画

Last Updated: Jun 28, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

科学分野:

  • コンピュータサイエンス
  • 人工知能
  • 機械学習

背景:

  • 転移学習は、限られたトレーニングデータでの画像分類に不可欠です。
  • 既存のアンサンブル方法は、効率と精度の向上のために改善される可能性があります。

研究 の 目的:

  • 転移学習を使用した新しい適応アンサンブル法を提案すること。
  • 象の群れの最適化（EHO）を使用して分類器の選択を最適化することによって、画像分類パフォーマンスを向上させること。

主な方法:

  • 転移学習を使用して複数の分類器を構築すること。
  • 確率的出力を単一の特徴行列に結合すること。
  • アンサンブルに最も効果的な分類器のサブセットを選択するために象の群れの最適化（EHO）を採用すること。

主要な成果:

  • 提案されたEHOベースの適応アンサンブル法は、画像分類精度を大幅に向上させます。
  • この方法は、分類器選択の冗長性を減らすことによって効率を向上させます。
  • GAITおよびODIR-5Kデータセットでの実験結果は、古典的なアンサンブル戦略と比較して優れたパフォーマンスを示しています。

結論:

  • 新しい適応アンサンブルアプローチは、優れた画像分類のために転移学習とEHOを効果的に活用します。
  • この方法は、限られたトレーニングデータがあるシナリオに対して堅牢なソリューションを提供し、従来のアンサンブル技術を上回ります。