] The primary purpose of the supplemental is to provide the theoretical arguments that our algorithm is correct. We first give the proof that our proposed tree metric is indeed tree additive. We then analyze the consistency of Algorithm 1. 1 Path Additivity We first prove that our proposed tree metric is path additive based on the proof technique in Song et al. (2011). Lemma 1. If Assumption 1 ...

Generalized additive models have been popular among statisticians and data analysts in multivariate nonparametric regression with non-Gaussian responses including binary and count data. In this paper, a new likelihood approach for fitting generalized additive models is proposed. It aims to maximize a smoothed likelihood. The additive functions are estimated by solving a system of nonlinear inte...

We show a new algorithm and its implementation for multiplying bitpolynomials of large degrees. The algorithm is based on evaluating polynomials at a specific set comprising a natural set for evaluation with additive FFT and a high order element under Frobenius map of F2. With the high order element, we can derive more values of the polynomials under Frobenius map. Besides, we also adapt the ad...

Many large Markov decision processes (MDPs) can be represented compactly using a structured representation such as a dynamic Bayesian network. Unfortunately, the compact representation does not help standard MDP algorithms, because the value function for the MDP does not retain the structure of the process description. We argue that in many such MDPs, structure is approximately retained. That i...

This paper proposes a new method for online secondary path modeling (OSPM) in active noise control (ANC) systems. The proposed method comprises two adaptive filters: a noise control filter and an OSPM filter. The noise control filter is adapted by the FxLMS algorithm, and using the same error signal as used in the adaptation of the OSPM filter. The OSPM filter is adapted using a variable step s...

To some extend, many complicated nonlinear maps are additive models of a number of linear and nonlinear terms. A single linear model or nonlinear model (i.e., a neural network model) has its limitation for approximating this class of maps. In this paper, a hybrid approach to evolve an additive tree model for a given problem is proposed. In this approach, tree-structure based evolution algorithm...

If the promise of computational modeling is to be fully realized in higher-level cognitive domains such as language processing, principled methods must be developed to construct the semantic representations that serve as these models’ input and/or output. In this paper, we propose the use of an established formalism from mathematical psychology, additive clustering, as a means of automatically ...

Additive isotonic regression attempts to determine the relationship between a multi-dimensional observation variable and a response, under the constraint that the estimate is the additive sum of univariate component effects that are monotonically increasing. In this article, we present a new method for such regression called LASSO Isotone (LISO). LISO adapts ideas from sparse linear modelling t...

We study average consensus when, simultaneously, the topology is random (links are offline or onlineat random times) and the communication among sensors is corrupted by additive noise. Additive noisecauses the states of the standard average consensus algorithm to diverge. To overcome this, we considertwo modifications to average consensus: 1) the A−ND algorithm with weights decaying...

A model-based spectral estimation algorithm is derived that improves the robustness of speech recognition systems to additive noise. The algorithm is tailored for filter-bank-based systems, where the estimation should seek to minimize the distortion as measured by the recognizer's distance metric. This estimation criterion is approximated by minimizing the Euclidean distance between spectral lo...