Motivated by a statistical inference perspective based on a likelihood-ratio test, Koestinger et al. [45] adopt equivalence constraints to learn a metric model called KISSME (keep it simple and straightforward metric).
We use lasso regression with positive constraints to learn model parameters that represent binding probabilities to individual k-mers, where k is determined as part of the learning procedure.
This appeal to learning constraints of adult learners as an explanation for morphological simplification has also been proposed by the descriptive analyses of Trudgill [29] and McWhorter's ("interrupted transmission" hypothesis) [7] which has been previously supported only by selected examples.
This paper proposes a unified approach to learning from constraints, which integrates the ability of classical machine learning techniques to learn from continuous feature-based representations with the ability of reasoning using higher-level semantic knowledge typical of Statistical Relational Learning.
In our setting, resource constraints present an additional challenge to learning universal standards of health professionalism.
Though developments in technology have overcome the constraints on learning space, an inability to appropriately exploit the technology may make it an obstacle to learning instead.
Secondly, according to learning behavior characteristics of four types of the learning scenarios, a multi-constraint learning path recommendation model is proposed, in which the variables and their weighted coefficients considers different learning path preferences of the learners in different learning scenarios as well as learning resource organization and fragmented time.
(iii) Add to the resulting program new variables and corresponding constraints to represent the experiment to learn (e ^ ) as well as its readouts under each copy (r M 1, e ^, r M 2, e ^ ).
These findings call for nontrivial constraints to be incorporated into learning rules, such as split constraints for ON- and OFF-center afferents, to allow learning models to develop simple-cell receptive fields [121].
As in GBNet [ 5], the gene category labels and promoter sequences (600 bps upstream of the start codon) are fed into UMMI to learn the sequence constraints.
