Sentence examples for machine learning deals from inspiring English sources

Traditionally, numerical machine learning deals with unstructured data, in the form of vectors: neural networks, graphical models, support vector machines, handle vectors of features that are assumed to be relevant for solving the problem at hand (classification or regression).

The robot uses webcams, too, for visual input, and a depth sensor, and it makes use of machine learning to deal with both a requirement to be continually moving, while at the same time dealing with the highly unpredictable movement of people moving around in crowds.

One of the most important challenges in machine learning is dealing with concept changes.

Also, POS tagged data are a relatively big test bed for machine learning methods dealing with sequence classification.

On the other hand machine learning approaches can deal with process optimizations and outcome predictions by extracting useful information and correlations from huge datasets built from experiments at different length scales and in heterogeneous conditions.

Reinforcement learning is a machine learning technique that deals with sequential control problems [24].

In this paper we present a Scalable Real-time Fraud Finder (SCARFF) which integrates Big Data tools (Kafka, Spark and Cassandra) with a machine learning approach which deals with imbalance, nonstationarity and feedback latency.

Meanwhile, SVMs outperform other machine learning techniques when dealing with small sample size.

It would be useful to investigate how GP can be combined with other machine learning techniques to deal with multiple decisions in production planning and scheduling.

It is thus paramount to develop and employ effective machine learning techniques to deal with such high dimensional and large datasets generated from the CAS area.

The methodology followed to address this problem consisted of three steps: (1) the calculation of gene pair features to be combined, (2) the building of the classification model using machine learning algorithms to deal with Big Data from a pairwise dataset, and (3) the classification of related gene pairs.