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関連する概念動画

The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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The Representativeness Heuristic02:13

The Representativeness Heuristic

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Position of Equilibrium in Acid-Base Reactions02:05

Position of Equilibrium in Acid-Base Reactions

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In any solution, the value of pKa indicates whether an acid is completely dissociated or not. A negative pKa corresponds to a stronger acid, whereas a positive pKa corresponds to a weaker acid. Consider the reaction between ammonia and an ethoxide ion. In this reaction, ethanol with a pKa of 15.9 is a stronger acid than ammonia with a pKa of 38. Recall that the strong acid forms a weak conjugate base, and a weak acid forms a strong conjugate base. Hence, the ethoxide ion is a weak base.
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Classification of Titrimetric Analysis Based on Reaction Types01:01

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Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
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Updated: Jan 25, 2026

Troubleshooting FoCUS Image Acquisition: Patient Positioning, Transducer Manipulation, and Image Optimization
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画像分類のための多様体ヒューリスティック最適化に基づく正のデータ拡張

Fangqing Liu, Han Huang, Fujian Feng

    IEEE transactions on pattern analysis and machine intelligence
    |January 23, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    この研究では、特徴分布を保存する新しいデータ拡張法を紹介します。多様体ヒューリスティック最適化アルゴリズム（MHOA）は、データの整合性を維持しながら正のサンプルを強化し、画像分類精度を向上させます。

    キーワード:
    データ拡張画像分類多様体仮説特徴分布機械学習

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    科学分野:

    • コンピュータサイエンス
    • 機械学習
    • 人工知能

    背景:

    • データ拡張は、特に正のサンプルにおいて、トレーニングデータが不十分な場合に不可欠です。
    • 既存の方法は、特徴分布の最適化をしばしば無視し、ニューラルネットワークのフィードバックに依存しています。

    研究 の 目的:

    • 実用的で分布を保存するデータ拡張パイプラインを開発すること。
    • 元のデータ分布との整合性を維持しながら、正のサンプルを拡張すること。

    主な方法:

    • 多様体仮説に触発された多様体ヒューリスティック最適化アルゴリズム（MHOA）を提案。
    • オブジェクトの輪郭ピクセルの周りの低次元ユークリッド空間を探索することにより、サンプルを拡張。
    • 元のデータ多様体に対する特徴指標の忠実度を最適化し、特徴統計量が整合されたサンプルを保持した。

    主要な成果:

    • 様々なニューラルネットワークにわたる画像分類精度の大幅な向上。
    • 特にガウス分布の特徴指標において、最先端のデータ拡張法を上回る性能を示した。
    • 主要な特徴ピクセルの近傍に焦点を絞った探索空間によって駆動される優れたパフォーマンスを実証した。

    結論:

    • MHOAパイプラインは、分布を保存するデータ拡張のための効果的な戦略を提供します。
    • このアプローチは、特徴分布の忠実度を最適化することにより、モデルのパフォーマンスを向上させます。
    • この方法は、ガウス特徴分布を持つデータセットで特に有望です。