We report extensive results over a collection of machine learning benchmark databases.
The performance of OMLA has been compared with both the existing online learning and batch learning algorithms for spiking neural networks using the UCI machine learning benchmark data sets.
In this section, we begin by briefly describing the resources used for the experiments: the GO, the GOA database that provided both the knowledge base needed for the machine learning and the benchmarks needed for the evaluation, and the BioCreative I test set that was a supplementary benchmark for our evaluations.
We use applications from the networking, media and machine learning domains to benchmark our techniques.
And by using waypoints and benchmarks, machine learning technologies are able to tell if information is coming from legitimate sources, or sources devised to spread misinformation.
With the advent of image datasets and benchmarks, machine learning and image processing have recently received a lot of attention.
All benchmark machine learning methods were trained on the same input data as described for our method (normalised adjacency with enriched properties of known causes of each ADR).
Using a reasonably accurate fatiguing leaky integrate and fire (FLIF) neural model, and biologically plausible compensatory Hebbian learning rules, simulations categorise benchmark machine learning data.
In the case of the latter application, numerous experimental results on benchmark machine learning datasets show that our approach exceeds the performance of current state-of-the-art methods, while requiring a fraction of the computation time.
The main purpose of this study was to classify apple yield using an efficient FCM learning algorithm, the non-linear Hebbian learning, and to compare it with the conventional FCM tool and benchmark machine learning algorithms.
