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相关概念视频

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

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Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Classification of Systems-II01:31

Classification of Systems-II

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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,
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相关实验视频

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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基于HSIC和子搜索算法的多标签功能选择.

Tinghua Wang1, Huiying Zhou1, Hanming Liu1

  • 1School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China.

Mathematical biosciences and engineering : MBE
|September 7, 2023
PubMed
概括

本研究引入了一种使用希尔伯特-施密特独立性标准 (HSIC) 和子搜索算法 (SSA) 的新型多标签特征选择方法. 该方法有效地解决了多标签学习任务中维度的诅咒.

科学领域:

  • 机器学习 机器学习
  • 数据挖掘 数据挖掘
  • 人工智能的人工智能

背景情况:

  • 多标签学习涉及每个样本都有多个相关标签的数据集.
  • 这些标签往往表现出相互依赖,使分析复杂化.
  • 这是一个很棒的节目,这是一个很棒的节目.

研究的目的:

  • 提出一个有效的多标签特征选择方法.
  • 为了应对面临的挑战的世界.

主要方法:

  • 使用子搜索算法 (SSA) 进行高效的特征子集搜索.
  • 采用希尔伯特-施密特独立性标准 (HSIC) 作为核心标准.
  • HSIC量化了特征和所有标签之间的依赖,以指导选择.

主要成果:

  • 实验验证证证实了拟议方法的有效性.
  • 该方法成功地确定了最佳特征子集.
  • 在处理多标签数据方面显著改进.

结论:

  • 提出的基于HSIC和SSA的方法对于多标签特征选择是有效的.
关键词:
希尔伯特 - 施密特独立性标准 (HSIC)数据挖掘是数据挖掘的一个方法.功能选择 功能选择多个标签的分类.小搜索算法搜索算法

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  • 为这个领域的维度诅咒提供了一个强有力的解决方案.
  • 为机器学习从业者提供了一个有价值的工具.