Examples of using Machine learning techniques in English and their translations into Portuguese
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Assessed the use of machine learning techniques to predict graft survival11.
This work aims to automate compiler adjustment process through machine learning techniques.
This project proposes machine learning techniques as a new tool for this scenario.
The classification model is based on the use of rules which are weighted by applying machine learning techniques.
Recently the machine learning techniques(ml) were inserted for estimation of hb.
To the automatization of this task, most of the works have used machine learning techniques, mainly from the supervi- sed paradigm.
We use machine learning techniques on certain data in order to optimize our marketing campaigns.
This paper presents a literature review of approaches that explored to apply machine learning techniques for drawing graphs.
Security experts use various machine learning techniques to gather the data and determine a normal system behavior.
When there's lots of data in tabular form, Wolfram NLU looks at whole columns etc. together,and uses machine learning techniques to adapt and optimize the interpretations it gives.
Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.
In this thesis, we explore the advantages of network data representation to develop machine learning techniques based on dynamical processes on networks.
Many machine learning techniques easily scale to multiple machines instead of a single, expensive high-end platform.
In particular, the approaches based on machine learning techniques have shown large interest among researchers.
Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion.
Articles were sorted into categories using machine learning techniques, and feature weight statistics were calculated using tf-idf.
Machine learning techniques are focused on the importance of the predictor and the targeted response instead of turning it only to statistical analysis for p values.
This may involve gaining greater experience with machine learning techniques like"deep learning," which mimic how the human brain operates.
Machine learning techniques usually learn some decision surface that separates samples from dierent classes by means of their vectorial representation.
FooView will utilize the internal power of your smart phones using machine learning techniques, save 80% of your operations, let everything be simple.
Although numerous machine learning techniques have been developed for attacking this problem, most of them work with no prior knowledge of the data.
David Press, trust data scientist at Airbnb,wrote about how they leverage machine learning techniques to identify and block fraudsters while minimizing impact on good users.
Recommender systems are used to provide information orproducts for users by learning the profile of their users automatically using machine learning techniques.
To meet this challenge,there are machine learning techniques that have similarities to the learning of a human being.
This model of operator is called template matching adaptive morphological operator(omacp), andcombines the formalism of mathematical morphology through eluts(elementary look-up tables) with machine learning techniques.
Relational learning to extend machine learning techniques to deal with expressive logical or relational representations.
The machine learning courses cover both theory and practice andaddress a wide spectrum of machine learning techniques in classification, regression and unsupervised learning settings.
In this case we can use machine learning techniques. its algorithms allow a computer to learn and classify patterns safely and fast.
One of the use cases of Big Data in medicine is the application of machine learning techniques to predict the likelihood of events based on continuous data streams.
In this context, machine learning techniques are attractive for the development of automated decision support systems, as they enable the creation of powerful classification models from problem representative data sets.