Mar 9, 2015 · The experimental results on three popular hyperspectral images show that FEWT has better performance and more speed than some state-of-the-art ...
In the FEWT, the relative importance of each feature of a training sample in predicting the class label of that sample is obtained and considered as a weight ...
Abstract. This study investigates a new method of feature extraction for classification problems with a considerable amount of outliers. The.
Nov 2, 2017 · I am training a model with Tensorflow Estimator, and my data is not balanced. I want to correct for this by weighting each training example.
This study investigates a new method of feature extraction for classification problems with a considerable amount of outliers. The method is a weighted ...
Oct 4, 2023 · There's actually been a good amount of work on sample reweighting, namely learning a set of weights for each datapoint in your training data.
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Jun 22, 2020 · Simply put, you give each sample weight, and then you create your dataset by sampling them with replacement. This means that instances with higher weights may ...
Oct 22, 2024 · This study investigates a new method of feature extraction for classification problems with a considerable amount of outliers.
May 7, 2023 · Feature extraction with deep neural networks · Trained deep neural networks are capable of extracting significant features from the input data.
From our analysis, we derive a weighting algorithm that is able to select and linearly transform variables by fixing the dimensionality of the space where a ...