Sentence examples for excessive learning from inspiring English sources

Exact(1)

Another problem is overfitting, which means that the learner, due to sparse data, complex model or excessive learning procedure, describes noise or random features in the training dataset, instead of the real phenomenon.

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Back in the 19th century, Harvard physician Edward H. Clarke argued that excessive book learning rendered women infertile, diverting energy from their wombs to their brains, giving them "monstrous brains and puny bodies".

More seriously, an accumulation of evidence has suggested that excessive habit learning is central to disorders of compulsivity, including obsessive-compulsive disorder (OCD; Gillan & Robbins, 2014) and substance dependence (Dickinson, Wood, & Smith, 2002; Sjoerds et al., 2013), which are characterized by cycles of maladaptive, repetitive behavior.

Lacking links can be supplemented by excessive links or learning links.

In machine learning, excessive features degrade the performance of machine learning algorithms due to over-fitting problems [17].

MUHAMMADU BUHARI is fast learning what excessive expectations can do.

Cortisol has been shown to damage and kill cells in the hippocampus (the brain area responsible for your episodic memory) and there is robust evidence that excessive cortisol shuts down learning, creates anxiety attacks, can cause depression, and premature brain aging.

There is extensive literature on the link between absenteeism and lack of subject matter interest, poor teaching strategies, unfavorable learning environment, excessive socialization among students, part-time jobs, ill health, sleeplessness, and poor relations with lecturers [ 1, 2, 4- 6].

If his vigil seems excessive, he says, he learned the hard way about missing out on Abba events.

In contrast, copying is redundant or excessive in pure patches where learning is easy, and mostly ineffective in uniform environments where learning is difficult.

Other methods treat these networks in a global manner (instead of fitting logic functions node by node) borrowing ideas from optimization and machine learning to avoid excessive model complexity (Bonneau et al., 2006; Saez-Rodriguez et al., 2009).

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