Results 1  10
of
2,194,904
Exemption from Particular Classes Policy
"... A. For all students to achieve all planned educational outcomes B. For students to be part of the school community and to form cohesive class groups C. To respect beliefs of individual families D. To cater for individual capabilities and interests II. Principles A. As much as possible we wish all st ..."
Abstract
 Add to MetaCart
grounds for exemption, however if it can be shown that an alternative individual program would be in the best interests of the student and could be achieved without undue hardship to the teacher and the class, it will be considered. This is particularly relevant for extension work. E. Exemptions may
Generating particular classes of connected graphs
, 2009
"... We present an algorithm to generate all connected graphs in a recursive and efficient manner. This algorithm is restricted subsequently to 1particle irreducible graphs and to connected graphs without selfloops. The recursions proceed by loop order and vertex number. The main result of [1] is a recu ..."
Abstract
 Add to MetaCart
We present an algorithm to generate all connected graphs in a recursive and efficient manner. This algorithm is restricted subsequently to 1particle irreducible graphs and to connected graphs without selfloops. The recursions proceed by loop order and vertex number. The main result of [1] is a recursion formula to generate all tree graphs. The underlying structure is a Hopf algebraic representation of graphs defined in the context of quantum field theory. This recursion formula is generalized to all connected graphs in [2]. In both cases, graphs are generated together with weight factors, which correspond to the inverses of their symmetry factors as considered in [3, 4]. In this paper, we reformulate that recursion formula to generate all connected graphs, in terms of abstract graphs. In addition, we restrict this result successively to 1PI graphs and to connected graphs without selfloops. Crucially, as in [1, 2], the correct weights of graphs are obtained. The (double) recursions proceed by loop order and vertex number. Our method is based on three basic operations on graphs, which are suitable to produce graphs with, say, e internal edges from a graph with e−1 internal edges. Namely, attaching a selfloop to a vertex; connecting a pair of adjacent vertices with an edge; and splitting a vertex in two, distributing
On the Solution Sets of Particular Classes of Linear Interval Systems
 J. Comput. Appl. Math
, 1996
"... We characterize the solution set S of real linear systems Ax = b by a set of inequalities if b lies between some given bounds b; b and if the n × n coefficient matrix A varies similarly between two bounds A and A. In addition, we restrict A to a particular class of matrices, for instance t ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
We characterize the solution set S of real linear systems Ax = b by a set of inequalities if b lies between some given bounds b; b and if the n × n coefficient matrix A varies similarly between two bounds A and A. In addition, we restrict A to a particular class of matrices, for instance
On the Solution Sets of Particular Classes of Linear Systems
, 1996
"... We characterize the solution set S of real linear systems Ax = b by a set of inequalities if b lies between some given bounds b; b and if the n × n coefficient matrix A varies similarly between two bounds A and A. In addition, we restrict A to a particular class of matrices, for instance t ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
We characterize the solution set S of real linear systems Ax = b by a set of inequalities if b lies between some given bounds b; b and if the n × n coefficient matrix A varies similarly between two bounds A and A. In addition, we restrict A to a particular class of matrices, for instance
A PARTICULAR CLASS OF SOLUTIONS OF A SYSTEM OF EIKONAL EQUATIONS
, 2010
"... particular class of solutions of a system of eikonal equations ..."
ClassBased ngram Models of Natural Language
 Computational Linguistics
, 1992
"... We address the problem of predicting a word from previous words in a sample of text. In particular we discuss ngram models based on calsses of words. We also discuss several statistical algoirthms for assigning words to classes based on the frequency of their cooccurrence with other words. We find ..."
Abstract

Cited by 961 (5 self)
 Add to MetaCart
We address the problem of predicting a word from previous words in a sample of text. In particular we discuss ngram models based on calsses of words. We also discuss several statistical algoirthms for assigning words to classes based on the frequency of their cooccurrence with other words. We
The 2005 pascal visual object classes challenge
, 2006
"... Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not presegmented objects). Four object classes were selected: motorbikes, bicycles, cars and peop ..."
Abstract

Cited by 633 (24 self)
 Add to MetaCart
Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not presegmented objects). Four object classes were selected: motorbikes, bicycles, cars
The PASCAL Visual Object Classes (VOC) challenge
, 2009
"... ... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has be ..."
Abstract

Cited by 624 (20 self)
 Add to MetaCart
... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the stateoftheart in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
Results 1  10
of
2,194,904