Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Data Science
With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions t...
Science
The concept is fundamental in statistics and tailors to the emergence of collective behaviours. Communication then asks for uncertainty considerations - noise, indeterminacy or approximation - and its wider impact on the couple perception-action. Clustering being all about uncertainty handling, data set representation appears not to be the only solution: Introducing hierarchies with adapted metrics, a priori pre-improving the data resolution are other methods in need of evaluation. The technology together with increasing semantics enables to involve synthetic data as simulation results for the multiplication of sources. Part B plays with another couple important for complex systems: state vs. transition. State-first descriptions would characterize physics, while transition-first would fit biology. That could stem from life producing dynamical systems in essence.
Advances in Logic Based Intelligent Systems
LAPTEC' 2005 promoted the discussion and interaction between researchers and practitioners focused on both theoretical and practical disciplines concerning logics applied to technology, with diverse backgrounds including all kinds of intelligent systems having classical or non-classical logics as underlying common matters.
LATIN '92
This volume contains the proceedings of LATIN '92, a theoretical computer science symposium (Latin American Theoretical Informatics) held in S o Paulo, Brazil in April 1992. LATIN is intended to be a comprehensive symposium in the theory of computing, but for this first meeting the following areas were chosen for preferential coverage: algorithms and data structures, automata and formal languages, computability and complexity theory, computational geometry, cryptography, parallel and distributed computation, symbolic and algebraic computation, and combinatorial and algebraic aspects of computer science. The volume includesfull versions of the invited papers by 11 distinguished guest lecturers as well as 32 contributed papers selected from 66 submissions from authors with affiliations in 26 countries.
Proceedings, 2019, MaxEnt 2019
This Proceedings book presents papers from the 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2019. The workshop took place at the Max Planck Institute for Plasma Physics in Garching near Munich, Germany, from 30 June to 5 July 2019, and invited contributions on all aspects of probabilistic inference, including novel techniques, applications, and work that sheds new light on the foundations of inference. Addressed are inverse and uncertainty quantification (UQ) and problems arising from a large variety of applications, such as earth science, astrophysics, material and plasma science, imaging in geophysics and medicine, nondestructive testing, density estimation, remote sensing, Gaussian process (GP) regression, optimal experimental design, data assimilation, and data mining.
LATIN ...
None
Bayesian Inference and Maximum Entropy Methods in Science and Engineering
All papers were peer-reviewed. For over 25 years the MaxEnt workshops have explored Bayesian and Maximum Entropy methods in scientific, engineering, and signal processing applications. This proceedings volume covers all aspects of probabilistic inference such as techniques, applications, and foundations. Applications include physics, space science, earth science, biology, imaging, graphical models and source separation.
Aprendizado de máquina: uma abordagem estatística
Aprendizado de máquina (AM) nasceu na década de 60 como um campo da inteligência artificial que tinha o objetivo de aprender padrões com base em dados. Originalmente, as aplicações de AM eram de cunho estritamente computacional. Contudo, desde o final dos anos 90, essa área expandiu seus horizontes e começou a se estabelecer como um campo por si mesma. Em particular, as aplicações de AM começaram a ter muitas intersecções com as de estatística. A comunidade de AM é bastante interdisciplinar e utiliza ideias desenvolvidas em diversas áreas, sendo a estatística uma delas. Enquanto que até os anos 90 métodos criados pela comunidade estatística rapidamente começavam a ser incorporados em AM, mais recentemente o fortalecimento de AM fez com que a direção oposta começasse a ficar cada vez mais comum: métodos desenvolvidos por AM começaram a ser usados em estatística. O objetivo deste livro é introduzir ideias de AM sob uma ótica estatística, quebrando barreiras entre essas duas áreas. Para a versão mais recente, veja http://www.rizbicki.ufscar.br/ame/
Mathematical Reviews
None
