TRAINING RELATING CRUSHING MACHINES

Brand Owner Address Description
RM Rubble Master HMH GmbH Im Südpark 196 A-4030 Linz Austria Training relating to crushing machines, sifting installations and conveyors;Downloadable and recorded software for operating crushing machines, sifting installations and conveyors; weight and volume measuring apparatus for crushing machines, sifting installations and conveyors; scales for crushing machines, sifting installations and conveyors;Crushing machines for crushing rocks, gravel, construction and demolition waste, namely,  asphalt, concrete with rebar, natural rock, glass, coal, and slag; wear segments for crushers being structural parts of crushing machines for crushing rocks and gravel; sifting installations, namely, sifting machines; stone-working machines;Repair or maintenance of crushing machines; rental of crushing machines, sifting installations and conveyors;RUBBLE MASTER;Science and technology services relating to crushing machines, sifting installations and conveyors, namely, scientific research and development; technological research and development services relating to crushing machines, sifting installations and conveyors; technological consultancy relating to crushing machines, sifting installations and conveyors, namely, engineering consulting; software as a service (SAAS) services featuring software for operating crushing machines, sifting installations and conveyors;
 

Where the owner name is not linked, that owner no longer owns the brand

    
Technical Examples
  1. A procedure for fast training and evaluation of support vector machines (SVMs) with linear input features of high dimensionality is presented. The linear input features are derived from raw input data by means of a set of m linear functions defined on the k-dimensional raw input data. Training uses a one-time precomputation on the linear transform matrix in order to allow training on an equivalent training set with vector size k instead of m, given a great computational benefit in case of m>>k. A similar precomputation is used during evaluation of SVMs, so that the raw input data vector can be used instead of the derived linear feature vector.