Titlebook: Artificial Intelligence Applications in Information and Communication Technologies; Yacine Laalaoui,Nizar Bouguila Book 2015 Springer Inte

PAGAN

1860-949X ld.Includes supplementary material: .This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation and Ontologies, and Multi-agent Systems. The main aim of this bo

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Benjamin D. Wright,A. Jackson Stennercomparison purposes. Experiments were conducted on CACM, RCV1 and random benchmarks. Numerical results show that ACO is scalable while achieving the same performance as the traditional IR process in terms of solutions quality.

paroxysm

美麗的寫

avulsion

Agustín Vicente,Fernando Martínez-Manriqueneously with model selection and parameters estimation in one single algorithm. The merits of RJMCMC for GID mixture learning is investigated using synthetic data and a real interesting application namely object detection.

嘮叨

Hybrid ACO and Tabu Search for Large Scale Information Retrievalcomparison purposes. Experiments were conducted on CACM, RCV1 and random benchmarks. Numerical results show that ACO is scalable while achieving the same performance as the traditional IR process in terms of solutions quality.

令人發(fā)膩

On the Application of Artificial Intelligence Techniques to Create Network Intelligence together with the main experimental results. This chapter will demonstrate various benefits achieved from adding an intelligent layer to ICT solutions, in various domains. Finally, we will also address future developments.

領(lǐng)導(dǎo)權(quán)

A Statistical Framework for Mental Targets Search Using Mixture Modelsantify the similarities between images. We run experiments including real users and we present a case study of a search process that gives promising results in terms of number of iterations needed to find the mental target classes within a given dataset.

d-limonene

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Variational Learning of Finite Inverted Dirichlet Mixture Models and ApplicationsDirichlet mixture which provides a natural way of clustering positive data. An EM-style algorithm is developed based upen variational inference for learning the parameters of the mixture model. The proposed statistical framework is applied to the challenging tasks of natural scene categorization and human activity classification.