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相关概念视频

Reinforcement01:23

Reinforcement

202
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Phylogenetic Trees03:21

Phylogenetic Trees

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.7K
Phylogeny01:23

Phylogeny

43.8K
Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
43.8K
Survival Tree01:19

Survival Tree

79
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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Reinforcement Schedules01:24

Reinforcement Schedules

142
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
142

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相关实验视频

Updated: Jun 24, 2025

A Practical Guide to Phylogenetics for Nonexperts
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A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

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树木重建游戏:使用强化学习的植物遗传重建.

Dana Azouri1,2, Oz Granit3, Michael Alburquerque2

  • 1School of Plant Sciences and Food Security, Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel.

Molecular biology and evolution
|June 3, 2024
PubMed
概括

这项研究引入了一种新的强化学习方法,用于遗传树的重建. 它优化了搜索策略,以找到全球最佳,显著提高了准确性和速度比目前的方法.

关键词:
人工智能的人工智能是人工智能.进化 演化 演化 演化 演化 演化 演化 演化机器学习是机器学习.分子生物学分子生物学人类遗传学 遗传学强化学习是一种强化学习.

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Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
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Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 遗传学树的重建在计算上具有挑战性,往往导致次优解决方案.
  • 当前的算法可能会陷入局部最佳状态,无法找到全球最适合的树.

研究的目的:

  • 开发一种新的范式,用强化学习来预测最大概率的家族遗传树.
  • 为了克服传统树搜索算法的局部最佳的局限性.

主要方法:

  • 采用强化学习来近似最佳搜索策略的长期概率收益.
  • 训练一个代理人,以导航向全球最佳的遗传学搜索空间.

主要成果:

  • 与实证数据上的最先进方法相比,实现了0.969或更高的日志概率改进.
  • 在数据集上演示了三倍的速度增长,数据集有15个序列,每个序列为18000 bp.
  • 减少了对昂贵的培训后可能性优化的需求.

结论:

  • 强化学习提供了一种强大的方法来增强家族遗传树的重建.
  • 拟议的方法显著提高了寻找最大概率树的准确性和效率.