Sentence examples for machine learning challenge from inspiring English sources

Google and Kaggle today announced a new machine learning challenge that asks developers to find the best way to automatically tag videos.

Learning from imbalanced data sets is an important machine learning challenge, especially in Support Vector Machines (SVM), where the assumption of equal cost of errors is made and each object is treated independently.

High dimensionality and classification of imbalanced data sets are two of the most interesting machine learning challenges.

Moreover, the surveyed methods also lack investigation into many common real-world machine learning challenges that may arise during transfer learning tasks.

Recently an international competition among AD predictors has been organized: "A Machine learning neuroimaging challenge for automated diagnosis of Mild Cognitive Impairment" (MLNeCh).

The analysis of physical datasets using modern methods developed in machine learning presents unique challenges and opportunities.

Looking at individual challenges, all machine learning approaches performed poorly for Challenge 36, which was a 3 peak spectrum of a substance typically measured in positive mode (see Additional file 1: Figure S8).

HackerRank HackerRank is a social platform for hackers to solve interesting challenges that include AI challenges, Machine learning problems, JavaScript hacks, image processing and many more.

Recent improvements in constructing accurate and scalable machine learning-based predictive models will be discussed in Section 2. Another pressing problem inherent in every machine learning application is the challenge of how to evaluate the predictive capability of the constructed models, in order to avoid stating over-optimistic prediction results [ 20].

Yet while the upward momentum is easy to detect, quantifying AI and machine learning investment has its challenges — it's not exactly a discrete sector.

In addition to distributed theoretical framework for machine learning to mitigate the challenges related to high volumes, some practicable parallel programming methods are also proposed and applied to learning algorithms to deal with large-scale data sets.