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Found 10 papers, 4 papers with code
Emerging Statistical Machine Learning Techniques for Extreme Temperature Forecasting in U.S. Cities
In this paper, we present a comprehensive analysis of extreme temperature patterns using emerging statistical machine learning techniques.
Efficient Novelty Detection Methods for Early Warning of Potential Fatal Diseases
The MIMIC II dataset was used to evaluate the performance of the proposed system.
Boosting the Predictive Accurary of Singer Identification Using Discrete Wavelet Transform For Feature Extraction
We conclude that, for a dataset of 4 singers and 200 songs, the best identification system consists of the DWT (db4) feature extraction introduced in this work combined with a linear support vector machine for identification resulting in a mean accuracy of 83. 96%.
A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data
This paper demonstrates the predictive superiority of discrete wavelet transform (DWT) over previously used methods of feature extraction in the diagnosis of epileptic seizures from EEG data.
Nonnegative Matrix Factorization with Zellner Penalty
Nonnegative matrix factorization (NMF) is a relatively new unsupervised learning algorithm that decomposes a nonnegative data matrix into a parts-based, lower dimensional, linear representation of the data.
Nonnegative Matrix Factorization with Toeplitz Penalty
We compare the facial recognition performance of our new Toeplitz Nonnegative Matrix Factorization (TNMF) algorithm to the performance of the Zellner Nonnegative Matrix Factorization (ZNMF) algorithm which makes use of data-dependent auxiliary constraints.
What do Asian Religions Have in Common? An Unsupervised Text Analytics Exploration
The main source of various religious teachings is their sacred texts which vary from religion to religion based on different factors like the geographical location or time of the birth of a particular religion.
Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning
The time series data are first mapped to highly discriminative features by applying dimensionality reduction based on temporal autocorrelation.
A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler
Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data.
Prediction Error Reduction Function as a Variable Importance Score
This paper introduces and develops a novel variable importance score function in the context of ensemble learning and demonstrates its appeal both theoretically and empirically.
