Sentence examples for hybrid learning from inspiring English sources

The training of the neuro-fuzzy system is done by a hybrid learning.

Both quantitative and qualitative data were collected from surveys designed to measure students' satisfaction and perspectives of hybrid learning.

Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training.

In this article, a hybrid learning architecture based on reinforcement learning (RL) and self-organizing neural networks for online adaptivity is presented.

The pair wants to find a way to make Abdalla's hybrid learning approach easier to pursue and available to any undergraduate at MIT.

A new hybrid learning method has emerged.

The ANFIS procedure may use a hybrid-learning algorithm to identify the membership function parameters of single-output, the Sugeno type fuzzy inference system.

As noted before, we adopt a hybrid learning-based and rule-based approach to temporal relation classification.

In this section, we describe our hybrid learning-based and rule-based approach to temporal relation classification.

See Table 1 below to distinguish the features between traditional in-person learning, web-enhanced, and blended/hybrid learning.

This paper presents the design of a hybrid learning model, termed as neural network tree (NNTree).