Search Results for author:
Found 7 papers, 7 papers with code
Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data
Logistic regression is a ubiquitous method for probabilistic classification.
QUANT: A Minimalist Interval Method for Time Series Classification
We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an 'off the shelf' classifier.
An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set
The measurement of progress using benchmarks evaluations is ubiquitous in computer science and machine learning.
HYDRA: Competing convolutional kernels for fast and accurate time series classification
We present HYDRA, a simple, fast, and accurate dictionary method for time series classification using competing convolutional kernels, combining key aspects of both ROCKET and conventional dictionary methods.
MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification
We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-of-the-art performance with a tiny fraction of the time and without the complex ensembling structure of many state-of-the-art methods.
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification
ROCKET achieves state-of-the-art accuracy with a fraction of the computational expense of most existing methods by transforming input time series using random convolutional kernels, and using the transformed features to train a linear classifier.
ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels
Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets.
