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Found 4 papers, 0 papers with code
Predicting Nearly As Well As the Optimal Twice Differentiable Regressor
We study nonlinear regression of real valued data in an individual sequence manner, where we provide results that are guaranteed to hold without any statistical assumptions.
A Novel Family of Adaptive Filtering Algorithms Based on The Logarithmic Cost
We introduce important members of this family of algorithms such as the least mean logarithmic square (LMLS) and least logarithmic absolute difference (LLAD) algorithms that improve the convergence performance of the conventional algorithms.
A Unified Approach to Universal Prediction: Generalized Upper and Lower Bounds
We first introduce the lower bounds on this relative performance in the mixture of experts framework, where we show that for any sequential algorithm, there always exists a sequence for which the performance of the sequential algorithm is lower bounded by zero.
A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees
In this paper, we investigate adaptive nonlinear regression and introduce tree based piecewise linear regression algorithms that are highly efficient and provide significantly improved performance with guaranteed upper bounds in an individual sequence manner.
