CONSTRUCTIVE FEATURES OF PLANTING MACHINES

Constructive Induction of Features for Planning

Constructive induction techniques use constructors to combine existing features into new features. Usually the goal is to improve the accuracy and/or efficiency of classification. An alternate use of new features is to create representations which allow planning in more efficient state spaces. An inefficient state space may be too fine grained, requiring deep search for plans with many steps, m...

Virtual Machines: Features and Futures

the impact of skopos on syntactic features of the target text

the present study is an experimental case study which investigates the impacts, if any, of skopos on syntactic features of the target text. two test groups each consisting of 10 ma students translated a set of sentences selected from advertising texts in the operative and informative mode. the resulting target texts were then statistically analyzed in terms of the number of words, phrases, si...

Learning Spam Features Using Restricted Boltzmann Machines

Nowadays, spam detection has been one of the foremost machine learning-oriented applications in the context of security in computer networks. In this work, we propose to learn intrinsic properties of e-mail messages by means of Restricted Boltzmann Machines (RBMs) in order to identity whether such messages contain relevant (ham) or non-relevant (spam) content. The main idea contribution of this...

Random Features for Large-Scale Kernel Machines

To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shiftinvariant kernel. We explore two sets of random features, provide conv...