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This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-worl...

Subjects / keywords: Computer science; Overfitting; Ensemble learning; Boosting (machine learning); AdaBoost; Artificial intelligence; Machine learning; Classifier (UML); Bayesian probability; Pattern recognition (psychology); Artificial neu...

Several interdisciplinary studies highlight imperfect information as a possible explanation of skill mismatches, which in turn has implications for unemployment and informality rates. Despite information failures and the...

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 ...

It is a privilege to present you with this stimulating book, which brings together an exceptional selection of ideas, experiences and reflections resulting from the meeting of a wide range of innovative educators from di...

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 investigated the longitudinal predictors of reading and spelling of words and pseudowords with different syllabic structures in a shallow orthography. Participants were 47 Spanish-speaking children from kindergarten t...

Q-Learning is a Reinforcement Learning method for solving sequential decision problems, where the utility of actions depends on a sequence of decisions and there exists uncertainty about the dynamics of the environment t...

During the last decade, the data sizes have grown faster than the speed of processors. In this context, the capabilities of statistical machine learning methods is limited by the computing time rather than the sample siz...

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science a...

Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, ...

El Aprendizaje por Refuerzo (RL) se ha convertido en una herramienta crucial para alinear modelos generativos complejos, superando las limitaciones de los métodos de aprendizaje supervisado tradicionales. Su capacidad p...

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning tec...

"The primary objective of this paper is to analyse the weaknesses, threats, strengths, and opportunities (SWOT matrix analysis) of online teaching and learning in educational institutions (primary, secondary, and higher ...

"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 ...

"Background Computer navigation during surgery can help oncologic surgeons perform more accurate resections. However, some navigation studies suggest that this tool may result in unique intraoperative problems and increa...

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...

The use of generic and generative methods for the development and application of interactive educational software is a relatively unexplored area in industry and education. Advantages of generic and generative techniques...

The problem of unwanted e-mails (or spam messages) has been increasing for years. Different methods have been proposed in order to deal with this problem wich includes blacklists of known spammers, handcrafted rules and ...

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...

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...

BACKGROUND: There is a growing recognition of the value of synthesising qualitative research in the evidence base in order to facilitate effective and appropriate health care. In response to this, methods for undertaking...

The production of educational contents in academic environments has been traditionally developed without an integral plan which allows their adequate distribution and access. In addition, it produces effort duplication a...

The paper shows that the teaching of reconfigurable systems is a complex and difficult process requiring many novel methods and tools to be considered within the same course. This process can be greatly simplified with t...