Learning constraint networks is known to require a number of membership queries exponential in the number of variables.
The problem of inductive inference refers to extracting general rules, learning concepts from a training set consisting of some fraction of the total number of possible examples of the concept.
The problem of inductive inference refers to extracting general rules, learning concepts from a training set consisting of some fraction of the total number of possible examples of the concept.
Continuous-variable dynamic systems, such as chemical refineries, electrical power plants, and the human cardiovascular system, exhibit time varying behavior through continuous changes in parameter
We propose a method to learn a diverse collection of discriminative parts from object bounding box annotations.
We performed an experiment to test learning in neural nets.
Multihop self-backhauling is a key enabling technology for millimeter wave cellular deployments.
Nonlinear energy harvesters (EH) behave differently depending on the range of their input power.
Viola and Jones (VJ) cascade classification methods have proven to be very successful in detecting objects belonging to a single class - e.g., faces.
Given the exponential increase in broadband cellular traffic it is imperative that scalable traffic measurement and monitoring techniques be developed to aid various resource management methods.
