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When the semi-supervised algorithm finishes, the active algorithm analyzes the instances that were not ranked and searches among them for the one that provides the largest information gain for the model.
Considering (X={x_1,ldots,x_n,ldots,x_N}) as the set of non-ranked instances and (k) the possible classes, the active algorithm must find an (x_i in X) that maximizes the amount information added to the system when it is classified as (k_j).
We prove that deterministic environments and a useful subclass of POMDP environments can be perfectly represented with equivalent compactness by such models and provide an active algorithm for autonomously learning such models in unknown environments from experience based on the biologically-inspired notion of surprises.
Similar(57)
Active algorithms of control units have got a notable complicity and a mutual interrelation.
The entire design and analysis procedure is carried out: the semi-active damper is characterized; a wide range of control strategies is recalled and an innovative semi-active algorithm (Mix-1-Stroke) based on a single-sensor layout is proposed.
In order to obtain an automatic detection procedure, an initialization of the Active Contours algorithm (AC-algorithm) is essential.
We propose here a new method for LV delineation using a three-dimensional heart-shaped LV model, an active shape algorithm and a volume-dependent algorithm for delineation of the endocardial surface.
The proposed active tracking algorithm is tested in simulation.
For each iteration, the active learning algorithm asks for the label of a single instance.
In addition, an active control algorithm for gust alleviation adaptively switching piezoelectric device functions is developed.
In this paper, we propose a novel active learning algorithm called Hessian optimal design (HOD).
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CEO of Professional Science Editing for Scientists @ prosciediting.com