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"A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and exible methods of ...

Ensembles of artificial neural networks (ANN) have been used in the last years as classification/regression machines, showing improved generalization capabilities that outperform those of single networks. We propose here...

We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold...

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly represen...

Subjects / keywords: Artificial intelligence; Deep learning; Computer science; Artificial neural network; Backpropagation; Reinforcement learning; Machine learning; Deep neural networks; Unsupervised learning; Recurrent neural network; Encod...

Learning algorithms for neural networks involve CPU intensive processing and consequently great effort has been done to develop parallel implemetations intended for a reduction of learning time. This work briefly descri...

Fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropiate tools for Intelligent Computer Systems. But on the other hand, learning alg...

Walter W. Powell, Kenneth W. Koput, Laurel Smith-Doerr, Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology, Administrative Science Quarterly, Vol. 41, No. 1 (Mar., 1996),...

En este libro se introducen los conceptos fundamentales del aprendizaje profundo (Deep learning, DL) mediante el uso de redes neuronales artificiales (Artificial neural networks, ANN). El lector podrá encontrar una revi...

Automático (Machine Learning). El objetivo del machine learning es que los sistemas informáticos sean capaces de aprender a partir de los datos, emulando de esta manera las capacidades humanas. El Aprendizaje Profundo ...

La inteligencia artificial permite la innovación y el cambio en todos los aspectos de la vida moderna. La mayoría de los avances actuales se basan en Deep Learning, un área de conocimiento muy madura que permite a las...

Research in English Language Teaching in Mexico has recently increased due to the new demanads imposed by international agencies for researchers to publish. As a result, networks and collaboration among institutions have...

An end-to-end methodology based on deep learning for the detection and localization of microcalcifications in digital mammograms introduces a novel methodology designed for the preprocessing and localization of clusters ...

Lo primero que hicimos para iniciar la redacción de este libro fue realizar distintos ejercicios de autorreconocimento de nuestras propias funciones. Para ello, nos apoyamos en la matriz A center for teaching and learni...

The communicators’ training is one the fields with more updates due to constant changes faced every year. Therefore, digital native students have competencies developed in an informal way that become a natural bridge t...

We are pleased to present to the entire academic community and the general public the following work, which contains recent research results of a group of teachers from the Faculty of Educational Sciences in areas such a...

It is a well-known fact, accepted by most psychologists, that students assimilate knowledge in different ways. Several categories of learners have been identified: oral, visual, kinetic. This has doubtless implications f...

The network society (Van Dijk, 2020) has completely blurred the traditional difference between virtual and real life, Our day to day is increasingly characterised by our continuous connection to the Internet, Multiple da...

Laboratory practice is a fundamental aspect of computer network learning. Experiments tend to be very specific, frequently demanding changes in the local network topology and privileged access to the operating system con...

Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neura...

Long short-term memory (LSTM; Hochreiter & Schmidhuber, 1997) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing...

With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding ...

A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Mach...

The growing number of control models based on combinations of neural networks, fuzzy systems and evolutionary algorithms shows that they represent a flexible and powerful approach. However, most of these models assume th...