by evaluating the trained node classifiers in a top-down fashion. To evaluate classifiers in our multipath framework, we define a new hierarchical loss function, the H-loss, capturing the intuition that whenever a classification mistake is made on a node of the taxonomy, then no loss should be charged for any

An algorithm for ranking spatial objects according to increasing distance from a query object is introduced and analyzed. The algorithm makes use of a hierarchical spatial data structure. The intended application area is a database environment, where the spatial data structure serves as an index

are semantically interpreted and arranged in a hierarchical fashion. Finally, a general-purpose ontology, WordNet, is trimmed and enriched with the detected domain concepts. The major novel aspect of this approach is semantic interpretation, that is, the association of a complex concept with a complex term

the matching measures. Our key contribution is a novel model that combines structured predictors for different feature descriptors in a hierarchical fashion, enabling accurate ranking. We demonstrate our method on an important application which consists of searching for people over short intervals of time

Recently, a new class of image coding algorithms coupling standard scalar quantization of frequency coefficients with tree-structured quantization (related to spatial structures) has attracted wide attention because its good performance appears to confirm the promised efficiencies of hierarchical

that a subset of data follows a Gaussian distribution. G-means runs k-means with increasing k in a hierarchical fashion until the test accepts the hypothesis that the data assigned to each k-means center are Gaussian. Two key advantages are that the hypothesis test does not limit the covariance

Abstract: This paper describes a layer model, temporally named as SAFEe-Learning Model, for designing e-Learning environment based on frameworks and models of instructional design applied in a hierarchical fashion. There have been a number of theories and models in the field of instructional design

it is possible to transmit less accurate, aggregated data and thus achieve better scalability. Using Active Networks, the aggregation computation can be done transparently by network routers without end system support. We present how aggregated data streams can be structured in a hierarchical fashion to allow

This paper presents an ICP-based algorithm for tracking an articulated skeletal model of the human body (stick figure) in 3D. The data are 3D points distributed roughly around the limbs ’ medial axes. The algorithm fits each stick to a limb in a hierarchical fashion, traversing the body’s kinematic

In a Cold Dark Matter (CDM) cosmology, small structures are the first to collapse and these then cluster together in a hierarchical fashion, giving rise to the bottom-up picture