Examples of using Statistical learning in English and their translations into Portuguese
{-}
-
Colloquial
-
Official
-
Medicine
-
Financial
-
Ecclesiastic
-
Ecclesiastic
-
Computer
-
Official/political
The nature of statistical learning theory.
In this category are grouped the online courses on statistical learning.
Both methodologies combine evolutionary sequence analysis with statistical learning methods random forest with feature selection, model tuning and robustness analysis.
Mind change bounds are closely related to mistake bounds that are studied in statistical learning theory.
Algorithmic learning theory is different from statistical learning theory in that it does not make use of statistical assumptions and analysis.
Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.
Uses==The VC dimension has utility in statistical learning theory, because it can predict a probabilistic upper bound on the test error of a classification model.
This new Master curriculum strongly focuses on Artificial Intelligence,including statistical learning and applications to data science.
Support Vector Machines(SVMs)is a statistical learning tool considered to be the state-of-the art classifiers for many applications today, including medical research and text categorization.
The programme contains four tracks: Computational Mathematics, Financial Mathematics, Optimization andSystems Theory, and Statistical Learning and Data Analytics.
This can be done using the modeling of knowledge andof reasoning mechanisms and/or by relying on statistical learning(from Bayesian models to Deep Learning) where the training data is either collected at once or on the fly.
Students participate in one or more working groups(research seminars), where they determine focus areas for an initial survey report andthen solve challenges at the intersection of cutting-edge research and technology in statistical learning theory.
Paper by Pai-Hsuen Chen, Chih-Jen Lin, and Bernhard Schölkopf,describing the main ideas of statistical learning theory, support vector machines(SVMs), and kernel feature spaces.
In statistical learning theory, or sometimes computational learning theory, the VC dimension(for Vapnik-Chervonenkis dimension) is a measure of the capacity(complexity, expressive power, richness, or flexibility) of a statistical classification algorithm, defined as the cardinality of the largest set of points that the algorithm can shatter.
École Polytechnique This new Master curriculum strongly focuses on Artificial Intelligence,including statistical learning and applications to data science.
The goal of the programme is to equip students with in-depth knowledge of relevant big data and statistical learning techniques, psychological measurement, and a broad set of practical skills so that they can successfully start a career in data science.
To gain this understanding, neuroscientists strive to make a link between observed biological processes(data), biologically plausible mechanisms for neural processing and learning(biological neural network models)and theory statistical learning theory and information theory.
Among the contents taught in the master can be found the necessary technologies for the analysis of massive data(big data), statistical learning and decision making, econometrics for massive data, mass data and business management, cloud computing(cloud computing), data mining and texts, analysis of time series, etc.
The knowledge you gain in for example survey methods and econometrics, are relevant to emerging areas such as data science and integration,big data and statistical learning, as well as others where proficient statisticians are required.
This concept emerges in a probabilistic(Bayesian) framework, where regularization can be performed by selecting a larger prior probability over simpler models;but also in statistical learning theory, where the goal is to minimize over two quantities: the'empirical risk' and the'structural risk', which roughly corresponds to the error over the training set and the predicted error in unseen data due to overfitting.
Methods for supervised and unsupervised statistical analysis andmodeling of biomedical data(including high-dimensional and time-to-event data), statistical learning, data mining, data integration, advanced computational statistics.
The question that has guided the research was:¿which contributions can the work with projects in mathematical modeling, mediated by the use of technologies,bring to students statistical learning in a business administration curse?¿.to answer this question, it has been used a qualitative approach, with the intention of considering more the process instead the final production.
Both lack in terms of providing theoretical guarantees while detecting drifts, once the first relaxes theassumption of data independency, required by the Empirical Risk Minimization Principle defined in the context of the Statistical Learning Theory, and the second fails due to no theoretical framework ensures learning, therefore detections are usually caused by the algorithm parametrization and not due to data changes.
Markov random field Statistical relational learning Probabilistic logic network Domingos, Pedro 2015.
High-quality information is typically derived through the devising of patterns andtrends through means such as statistical pattern learning.
The present study investigated the utilization of the dot plot andbox plot in the learning of statistical variability.
As a result, a myriad of state-of-the-art methods for ner andre relying on statistical machine learning techniques have been proposed in the literature.
Semantic mapping with statistical relational learning for knowledge representation….
And a minimum of 20 credits from this group, butstudents may not take both Statistical Machine Learning and Data Analysis and Modelling.