机器学习 Resources

Showing items tagged with "机器学习"

PNN, also known as Probabilistic Neural Network, was initially proposed by mathematician Specht in 1990 and subsequently refined by researchers including Master [1995]. This network architecture has been successfully applied across multiple domains including machine learning, artificial intelligence, and automatic control systems. Compared to multi-layer feedforward networks, PNN demonstrates simpler mathematical principles and easier implementation through its probability density function estimation approach using Parzen windows and radial basis functions.

MATLAB 237 views Tagged
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In machine learning, Random Forest is a classifier comprising multiple decision trees, where the output class is determined by the majority vote of individual tree predictions. Developed by Leo Breiman and Adele Cutler, this algorithm integrates "Bootstrap aggregating" and "random subspace method" for robust ensemble learning. This translation includes MATLAB-specific implementation insights for decision tree training, feature sampling, and aggregation techniques.

MATLAB 283 views Tagged
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Sparse coding algorithm implementation and its application in "self-taught learning" machine learning frameworks, which combines limited labeled data with abundant unlabeled data from different distributions.

MATLAB 233 views Tagged
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