constraint satisfaction backtracking algorithms, constraint learning is a technique for improving efficiency. It works by recording new constraints whenever...

backtracking "more than one variable" in some cases. Constraint learning infers and saves new constraints that can be later used to avoid part of the search...

very small number of constraints. There is always at least one constraint, and TOC uses a focusing process to identify the constraint and restructure the...

factorisation and various forms of clustering. Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional...

5947H. doi:10.4249/scholarpedia.5947. Rina Dechter (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

constraint solvers. The AC-3 algorithm is not to be confused with the similarly named A3C algorithm in machine learning. AC-3 operates on constraints...

semi-supervised learning algorithms. Typically, constrained clustering incorporates either a set of must-link constraints, cannot-link constraints, or both,...

Mutual exclusivity is a word learning constraint that involves the tendency to assign one label/name, and in turn avoid assigning a second label, to a...

and algorithms. Constraint solvers solve constraint satisfaction problems (CSPs). They support constraint programming. A constraint is a which must be...

is to apply a smoothness constraint or a regularization constraint to the flow field. One can combine both of these constraints to formulate estimating...

management triangle (called also the triple constraint, iron triangle and project triangle) is a model of the constraints of project management. While its origins...

In constraint programming and SAT solving, backjumping (also known as non-chronological backtracking or intelligent backtracking) is an enhancement for...

education (also known as online learning, remote learning or remote education) through an online school. A distance learning program can either be completely...

N} Local learning rate: η {\displaystyle \eta } Those parameters have to be optimized depending on the constraints of the machine learning application...

to enforce the constraint. In practice, this corresponds to performing the parameter update as normal, and then enforcing the constraint by clamping the...

A constraint in computer-aided design (CAD) software is a limitation or restriction imposed by a designer or an engineer upon geometric properties: 203 ...

Distributed constraint optimization (DCOP or DisCOP) is the distributed analogue to constraint optimization. A DCOP is a problem in which a group of agents...

Policy gradient methods are a class of reinforcement learning algorithms. Policy gradient methods are a sub-class of policy optimization methods. Unlike...

the project within a time constraint. Learning about the use of technology is a skill that can be gained through learning to use a variety of tools,...

Active learning is "a method of learning in which students are actively or experientially involved in the learning process and where there are different...

finding the local maxima and minima of a function subject to equation constraints (i.e., subject to the condition that one or more equations have to be...

Selection and Scheduling". In Lee, J. (ed.). Principles and Practice of Constraint Programming. Lecture Notes in Computer Science. Vol. 6876. pp. 454–469...

student's knowledge after one hour of learning (with the effect size of 0.6). COLLECT-UML COLLECT-UML is a constraint-based tutor that supports pairs of...

Chater, Nick (January 2016). "The Now-or-Never bottleneck: A fundamental constraint on language". Behavioral and Brain Sciences. 39: e62. doi:10.1017/S0140525X1500031X...

In constraint satisfaction, a decomposition method translates a constraint satisfaction problem into another constraint satisfaction problem that is binary...

In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations...

Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source)...

reflects bursts of learning following breakthroughs that make learning easier followed by meeting constraints that make learning ever harder, perhaps...

machine learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints. The...