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Found 6 papers, 2 papers with code
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting
We present coarse-to-fine autoregressive networks (C2FAR), a method for modeling the probability distribution of univariate, numeric random variables.
A Deep Learning Approach to Tongue Detection for Pediatric Population
The network classification accuracy was evaluated using leave-one-subject-out cross-validation.
Revisiting the Application of Feature Selection Methods to Speech Imagery BCI Datasets
Brain-computer interface (BCI) aims to establish and improve human and computer interactions.
Privacy-preserving feature selection: A survey and proposing a new set of protocols
However, traditional feature selection methods are only capable of processing centralized datasets and are not able to satisfy today's distributed data processing needs.
A Feature Selection Based on Perturbation Theory
Consider a supervised dataset $D=[A\mid \textbf{b}]$, where $\textbf{b}$ is the outcome column, rows of $D$ correspond to observations, and columns of $A$ are the features of the dataset.
A Fuzzy-Rough based Binary Shuffled Frog Leaping Algorithm for Feature Selection
This paper presents a new feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset.
