2,715 research outputs found
Multiclass Learning with Simplex Coding
In this paper we discuss a novel framework for multiclass learning, defined
by a suitable coding/decoding strategy, namely the simplex coding, that allows
to generalize to multiple classes a relaxation approach commonly used in binary
classification. In this framework, a relaxation error analysis can be developed
avoiding constraints on the considered hypotheses class. Moreover, we show that
in this setting it is possible to derive the first provably consistent
regularized method with training/tuning complexity which is independent to the
number of classes. Tools from convex analysis are introduced that can be used
beyond the scope of this paper
Reed-Solomon list decoding from a system-theoretic perspective
In this paper, the Sudan-Guruswami approach to list decoding of Reed-Solomon (RS) codes is cast in a system-theoretic framework. With the data, a set of trajectories or time series is associated which is then modeled as a so-called behavior. In this way, a connection is made with the behavioral approach to system theory. It is shown how a polynomial representation of the modeling behavior gives rise to the bivariate interpolating polynomials of the Sudan-Guruswami approach. The concept of "weighted row reduced" is introduced and used to achieve minimality. Two decoding methods are derived and a parametrization of all bivariate interpolating polynomials is given
Finite-Block-Length Analysis in Classical and Quantum Information Theory
Coding technology is used in several information processing tasks. In
particular, when noise during transmission disturbs communications, coding
technology is employed to protect the information. However, there are two types
of coding technology: coding in classical information theory and coding in
quantum information theory. Although the physical media used to transmit
information ultimately obey quantum mechanics, we need to choose the type of
coding depending on the kind of information device, classical or quantum, that
is being used. In both branches of information theory, there are many elegant
theoretical results under the ideal assumption that an infinitely large system
is available. In a realistic situation, we need to account for finite size
effects. The present paper reviews finite size effects in classical and quantum
information theory with respect to various topics, including applied aspects
Classification of Human Ventricular Arrhythmia in High Dimensional Representation Spaces
We studied classification of human ECGs labelled as normal sinus rhythm,
ventricular fibrillation and ventricular tachycardia by means of support vector
machines in different representation spaces, using different observation
lengths. ECG waveform segments of duration 0.5-4 s, their Fourier magnitude
spectra, and lower dimensional projections of Fourier magnitude spectra were
used for classification. All considered representations were of much higher
dimension than in published studies. Classification accuracy improved with
segment duration up to 2 s, with 4 s providing little improvement. We found
that it is possible to discriminate between ventricular tachycardia and
ventricular fibrillation by the present approach with much shorter runs of ECG
(2 s, minimum 86% sensitivity per class) than previously imagined. Ensembles of
classifiers acting on 1 s segments taken over 5 s observation windows gave best
results, with sensitivities of detection for all classes exceeding 93%.Comment: 9 pages, 2 tables, 5 figure
- …