Abstract. In this paper we elaborate on a kernel extension to tensorbased data analysis. The proposed ideas find applications in supervised learning problems where input data have ...
Marco Signoretto, Lieven De Lathauwer, Johan A. K....
This paper investigates a new learning model in which the input data is corrupted with noise. We present a general statistical framework to tackle this problem. Based on the stati...
While online service providers are sometimes accused of forwarding identifying customer information as name and address to untrusted third parties, comparatively little attention ...
A key challenge in applying kernel-based methods for discriminative learning is to identify a suitable kernel given a problem domain. Many methods instead transform the input data...
Abstract. In this paper, we give an overview of the Locomotive Simulater/Optimizer (LSO) decision support system developed by us for railroads. This software is designed to imitate...
Artyom G. Nahapetyan, Ravindra K. Ahuja, F. Zeynep...
Discrete event simulation (DES) projects rely heavily on high input data quality. Therefore, the input data management process is very important and, thus, consumes an extensive a...
This paper describes the optimisation of the word length in a 16-point radix-4 reconfigurable pipelined Fast Fourier Transform (FFT) based receiver device. Two forms of optimisati...
Empirical search is a strategy used during the installation of library generators such as ATLAS, FFTW, and SPIRAL to identify the algorithm or the version of an algorithm that del...
Most studies modeling inaccurate data in Gold style learning consider cases in which the number of inaccuracies is finite. The present paper argues that this approach is not reaso...