Abstract Object-oriented programming is as much a different way of designing programs as it is a different way of designing programming languages. This paper describes what it is like to design systems in Smalltalk. In particular, since a major motivation for object-oriented programming

. This paper describes the dataset and evaluation procedure. We review the state-of-the-art 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

, middle-class community participated in the project. Accelerated and at-risk third grade readers took home a tape recorder and a third grade science text to read aloud to mothers. The conversations were audiotaped, professionally transcribed, and then coded. Results of the study indicated

This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between

The primary objective of this course is to discuss the principals of finite difference methods and their applications in groundwater modeling. The emphasis of the class lectures is on the theoretical aspects of numerical modeling (finite difference method). Steps involved in simulation

in response to one class of stimuli implies the existence of multiple stable states and hysteresis in response to a different class of stimuli. The relation between these findings and a number of experiments is discussed.

differential or difference equation, the forward and backward shift operator, the Rodrigues-type formula and generating functions of all classes of orthogonal polynomials in this scheme. In chapter 2 we give the limit relations between different classes of orthogonal polynomials listed in the Askey

to their rates predicted assuming the star tree. Sites in different classes are then assumed to be evolving at these fixed rates when other tree topologies are evaluated.

We show how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin

Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than