Sequential Regression Learning with Randomized Algorithms
This provides an incremental improvement for machine learning applications dealing with sequential or time-series data.
The paper tackles the problem of learning from dynamic time-dependent data by introducing 'randomized SINDy', a sequential algorithm that uses a probabilistic approach with proven PAC learning guarantees. It demonstrates effectiveness in regression and binary classification on real-world data.
This paper presents ``randomized SINDy", a sequential machine learning algorithm designed for dynamic data that has a time-dependent structure. It employs a probabilistic approach, with its PAC learning property rigorously proven through the mathematical theory of functional analysis. The algorithm dynamically predicts using a learned probability distribution of predictors, updating weights via gradient descent and a proximal algorithm to maintain a valid probability density. Inspired by SINDy (Brunton et al. 2016), it incorporates feature augmentation and Tikhonov regularization. For multivariate normal weights, the proximal step is omitted to focus on parameter estimation. The algorithm's effectiveness is demonstrated through experimental results in regression and binary classification using real-world data.