In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

Grasping by a two-dimensional hand comprised of a palm and two hinged fingers i studied. The mathematics of frictionless grasping is presented and used in the development of a planner/simulator. The simulator computes the motion of the object using an active constraint set method and assuming exact knowledge of the physical properties of the polygonal object, hand, and support.

BACKGROUND The problem of people presenting to hospitals with self-harm is important because such presentations are common, there is a clear link to suicide and a high premature mortality. However, the best treatment for this population is unclear. AIMS To see whether a package of measures, that included regular postcards and problem-solving therapy, improved outcomes at 1 year compared with ...

UNLABELLED Planning the final aesthetic result of restorative treatment is a key skill needed by the clinician to ensure predictable success. This paper aims to review current techniques that may be used by clinicians for an aesthetic preview, whilst also highlighting a simple technique that may be readily utilized in practice. CLINICAL RELEVANCE It is considered good practice to give patient...

Active Learning Method (ALM) is a soft computing method which is used for modeling and control, based on fuzzy logic. Although ALM has shown that it acts well in dynamic environments, its operators cannot support it very well in complex situations due to losing data. Thus ALM can find better membership functions if more appropriate operators be chosen for it. This paper substituted two new oper...

This paper is concerned with the minimization of an objective that is the sum of a convex function f and an `1 regularization term. Our interest is in methods that incorporate second-order information about the function f to accelerate convergence. We describe a semi-smooth Newton framework that can be used to generate a variety of second-order methods, including block active-set methods, ortha...

Nonsmoothness pervades optimization, but the way it typically arises is highly structured. Nonsmooth behavior of an objective function is usually associated, locally, with an active manifold: on this manifold the function is smooth, whereas in normal directions it is “veeshaped.” Active set ideas in optimization depend heavily on this structure. Important examples of such functions include the ...