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Any realistic model of learning from samples must address the issue of noisy data.
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The Artificial Neural Network (ANN) is the most popular intelligent tool especially for non-linear function approximation and control applications due to its high ability of learning from sample data and yielding better results to the new data [20 22].
The intelligent features are achieved by means of a Neuro-Fuzzy system which has the ability to learn from samples, reason and adapt itself to changes in the environment or in user preferences.
The most representative methods in CI are fuzzy systems, able to perform imprecise reasoning; neural networks, which can learn from samples; and the genetic algorithms, which make a search in the space of solutions.
Motion patterns are represented using prototype trajectories which are learned from sample (previously observed) trajectories.
Although microdialysis in birds has been focused on the AFP, much could be learned from sampling from other structures, particularly those associated with motivated behaviors (e.g. POA, nucleus accumbens) or those with projections to the midbrain.
In this paper, we first propose a simple nonparametric statistical tool, based on the paired bootstrap resampling, to allow an operative result comparison among different learning-from-samples promotional models.
Humans typically deal with such edge cases remarkably well with no prior "training" by applying common sense or finding analogies and learning from these samples of one.
Artificial neural network (ANN) is an intelligent technique that can solve non-linear problems by learning from the samples.
Overcoming the drawbacks of threshold approach, artificial neural network may extract the symptom of the faults through learning from the samples, but it is difficult to design its structure.
HOW SHE GOT INTO REAL ESTATE The hard way, learning from Ms. Sample.
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