743,647 research outputs found
Classification
In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instances’’ from which it is expected to infer a way of classifying unseen instances into one of several ‘‘classes’’. Instances have a set of features or ‘‘attributes’’ whose values define that particular instance. Numeric prediction, or ‘‘regression,’’ is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with Data clustering (see entry Data Clustering), where the classes are not given, and with Association learning (see entry Association Learning), which seeks any association – not just one that predicts the class
Pose Induction for Novel Object Categories
We address the task of predicting pose for objects of unannotated object
categories from a small seed set of annotated object classes. We present a
generalized classifier that can reliably induce pose given a single instance of
a novel category. In case of availability of a large collection of novel
instances, our approach then jointly reasons over all instances to improve the
initial estimates. We empirically validate the various components of our
algorithm and quantitatively show that our method produces reliable pose
estimates. We also show qualitative results on a diverse set of classes and
further demonstrate the applicability of our system for learning shape models
of novel object classes
Concepts and their Use for Modelling Objects and References in Programming Languages
In the paper a new programming construct, called concept, is introduced.
Concept is pair of two classes: a reference class and an object class.
Instances of the reference classes are passed-by-value and are intended to
represent objects. Instances of the object class are passed-by-reference. An
approach to programming where concepts are used instead of classes is called
concept-oriented programming (CoP). In CoP objects are represented and accessed
indirectly by means of references. The structure of concepts describes a
hierarchical space with a virtual address system. The paper describes this new
approach to programming including such mechanisms as reference resolution,
complex references, method interception, dual methods, life-cycle management
inheritance and polymorphism.Comment: 43 pages. Related papers: http://conceptoriented.com
Tractable Optimization Problems through Hypergraph-Based Structural Restrictions
Several variants of the Constraint Satisfaction Problem have been proposed
and investigated in the literature for modelling those scenarios where
solutions are associated with some given costs. Within these frameworks
computing an optimal solution is an NP-hard problem in general; yet, when
restricted over classes of instances whose constraint interactions can be
modelled via (nearly-)acyclic graphs, this problem is known to be solvable in
polynomial time. In this paper, larger classes of tractable instances are
singled out, by discussing solution approaches based on exploiting hypergraph
acyclicity and, more generally, structural decomposition methods, such as
(hyper)tree decompositions
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