Examples of using Machine learning methods in English and their translations into Portuguese
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Machine learning methods for dense satellite image time series analysis.
Join a team of researchers andengineers with a proven track record in a variety of machine learning methods.
Machine learning methods for dense satellite image time series analysis, BP. TT.
A famous example from academia is the determination of the Higgs Boson from simulated data with machine learning methods.
Traditional machine learning methods are capable to assign only a single label to a new instance to be classified.
Join a team of researchers andengineers with a proven track record in a variety of machine learning methods.
Literature has widely applied machine learning methods in the inductive inference, primarily in the eld of bioinformatics.
The methods used to achieve this are well-known in computer science and mathematics andare often called"machine learning" methods.
Can implement common machine learning methods, and design and implement novel algorithms by modifying the existing approaches.
Genomic homology between cattle and buffaloes andcomparison of different machine learning methods for genotype imputation in buffalo genome, BE.EP. PD.
We studied the suitability of cn measurements fornatural language processing tasks, with classification being assisted by supervised and unsupervised machine learning methods.
Still, such studies consider that machine learning methods could provide flexible and workable tools to predict outcomes involving multiple variables.
This project will develop andapply novel information-theory-based measures of the brain connectivity and state-of-the-art machine learning methods to predict human intelligence from the brain c….
IT Researches provides a breadth of advanced machine learning methods that give predictions with the highest accuracy possible, surpassing what can be done with basic analytics.
In the context of time series prediction,these investments consist majority of grants for designed research aimed at adapting conventional machine learning methods for data analysis problems in which time is an important factor.
The analysis of data was performed by using two machine learning methods: decision tree and neural networks, in addition to visual evaluation through graphs and maps.
To improve the effectiveness of this information seeking task,systems have relied on the combination of many predictors by means of machine learning methods, a task also known as learning to rank l2r.
We dwelt at length on various methods: causal inference, machine learning, methods applied to hard-to-reach populations, mediation analysis and DAGs, and linkage of large databases.
Machine learning methods could help generate patterns of evolution based on large local and multicenter data sets, which would be very useful to improve quality of clinical decisions.
We present function models built by support vectors,a class of machine learning methods that can be used to pattern classification or regression.
Machine learning methods can be applied for multivariate analysis of neuroimaging data, however, they have been employed in most of the studies with main concern in group prediction, such as discriminating schizophrenic patients from healthy controls.
This work presents a systematic analysis of the application of machine learning methods to the algorithm selection problem using graph coloring as a case study.
Like other machine learning methods- systems that learn from data- neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.
Normally two of them, obstacle detection and road detection,make use of sophisticated algorithms such as supervised machine learning methods which can perform with impressive results if it was trained with good datasets.
Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as"unsupervised learning" or as a preprocessing step to improve learner accuracy.
During the summer period,you will concentrate on an independent research project which focuses on the application of machine learning methods, to original scientific problems provided by research groups from across the Faculty of Science.
The program focuses specifically on recent machine learning methods that not only have a wide range of applications in bioinformatics but have recently revolutionized the entire IT sector and are being viewed as key future technologies by Google, Facebook, Microsoft and many others.
Within the programme, you will learn(among other things) about the methods used by autonomous vehicles to know where they are, how the navigation software in your phone finds the best route, and the sensors that robots and intelligent systems use to perceive the world,as well as machine learning methods used by computers to make difficult choices or learn to recognise the patterns around them.
Is able to assess suitability of different machine learning methods for solving a particular new problem encountered in industry or academia, and apply the methods to the problem.
Data Science students will be armed with a solid knowledge of statistical and machine learning methods, optimization and computing, and the ability to spot, assess, and seize the opportunity of data-driven value creation.