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相关概念视频

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

823
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...
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Uniform Distribution01:19

Uniform Distribution

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The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
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Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
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Associative Learning01:27

Associative Learning

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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...
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相关实验视频

Updated: Sep 19, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

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深入研究可概括的标签 分发学习

Xingyu Zhao, Lei Qi, Yuexuan An

    IEEE transactions on pattern analysis and machine intelligence
    |June 19, 2025
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    概括
    此摘要是机器生成的。

    标签分发学习 (LDL) 方法在分发之外的场景中失败,原因是i.i.d. 假设.一个假设. 本研究通过探索域不变的特征标签相关性来提高性能,引入了可泛化标签分布学习 (GLDL).

    更多相关视频

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    Cross-Modal Multivariate Pattern Analysis
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    相关实验视频

    Last Updated: Sep 19, 2025

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
    07:31

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

    Published on: February 8, 2019

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    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

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    Cross-Modal Multivariate Pattern Analysis
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    科学领域:

    • 机器学习 机器学习
    • 人工智能的人工智能
    • 数据科学数据科学数据科学

    背景情况:

    • 标签分发学习 (LDL) 在处理标签模两可方面表现出色,但受到i.i.d.的限制. 假设.一个假设.
    • 现有的LDL方法在分布外数据上的性能下降,限制了实际应用.
    • 核心问题是标签注释在不同数据域中发生变化.

    研究的目的:

    • 为了解决标准LDL在分布之外的场景中的局限性.
    • 介绍和研究可泛化标签分发学习 (GLDL) 的问题.
    • 在各种数据领域开发强大的LDL的新方法.

    主要方法:

    • 研究了GLDL的特征,确定了特定域的标签注释转移.
    • 探索域不变特征标签相关性信息.
    • 提出了两种实际方法来缓解GLDL中的性能退化.

    主要成果:

    • 提出的方法在广泛的实验中显示出卓越的性能.
    • 这项研究验证了利用域不变特征标签相关性的有效性.
    • 这项研究为实际的GLDL问题提供了重大进展.

    结论:

    • 开发的GLDL方法有效地处理跨领域的标签模两可.
    • 这项工作为可通用标签分发学习建立了新的基准和技术.
    • 这些发现显著扩大了LDL在现实世界,多样化的数据集中的适用性.