Sentence examples for equivalence constraints from inspiring English sources

Koestinger et al. [29] presented KISSME method to learn a distance metric from equivalence constraints based on a statistical inference perspective.

Koestinger et al. [9] proposed the KISSME to learn an effective metric using equivalence constraints and had a good effect on the generalization performance.

We propose a principled approach for learning parameters in Bayesian networks from incomplete datasets, where the examples of a dataset are subject to equivalence constraints.

We further propose a new learning algorithm that can effectively learn more accurate Bayesian networks using equivalence constraints, which we demonstrate empirically.

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).

These equivalence constraints arise from datasets where examples are tied together, in that we may not know the value of a particular variable, but whatever that value is, we know it must be the same across different examples.

where two positive constants ρ, ζ are used to differently weigh the power flow constraint on z − (providing convergence on an approximate linear problem on power flow variables) from the full equivalence constraint on z +.

It argues that his conception of CS as a 'union' of, at least, two lexically-encoded grammars (Gs) constrained by the requirements of 'mixed Gs' is a Minimalist version of the Equivalence Constraint.

A partially invariant model (6b) relaxing the equivalence constraint for the items' loadings fit the data well: RMSEA = 0.035, CFI = 0.98, TLI = 0.98.

Except for configural invariance, partial invariance was explored, whenever complete invariance did not hold for a model, by relaxing the equivalence constraint for failing items (i.e., letting them free to vary across gender).

Less-than-or-equal and equality constraints are respectively accommodated by the equivalences, A·x≤b⇔−A·x≥−b and A·x=b⇔(A·x≥b)∧(A·x≤b).