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Subjects / keywords: Computer science; Mathematics; Set theory; Problems, exercises, etc; Algorithms; Logic, Symbolic and mathematical; Problems and exercises; Electronic books

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinatio...

Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typica...

The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order...

During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is ...

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up ...

Subjects / keywords: TREMBLAY, JEAN-PAUL; MCGRAW-HILL; 005.113 T789in

Subjects / keywords: Python (Computer program language); Data structures (Computer science); Computer algorithms

Subjects / keywords: Text processing (Computer science); Genetic algorithms; Computational linguistics

Subjects / keywords: Computer algorithms; Databases; Data structures (Computer science); Structured programming