Multiple-Instance learning is a way of mod-eling ambiguity in supervised learning examples. Each example is a bag of instances, but only the bag is labeled-not the individual instances. A bag is labeled negative if all the instances are negative, and positive if at least one of the instances in positive. We apply the Multiple-Instance learning framework to the problem of learning how to classif...

Received: 03 September 2014; accepted: 15 September 2014; published online: 09 October 2014. Citation: Lewicki MS (2014) Corrigendum: Scene analysis in the natural environment. Front. Psychol. 5:1119. doi: 10.3389/fpsyg.2014.01119 This article was submitted to Perception Science, a section of the journal Frontiers in Psychology. Copyright © 2014 Lewicki. This is an open-access article distribut...

It is now well-established that k nearest-neighbour classi"ers o!er a quick and reliable method of data classi"cation. In this paper we extend the basic de"nition of the standard k nearest-neighbour algorithm to include the ability to resolve con#icts when the highest number of nearest neighbours are found for more than one training class (model-1). We also propose model-2 of nearest-neighbour ...

In this paper, we consider applying multilayer, convolutional neural networks to construct a complete end-to-end text recognition system with performance comparable to the current state-of-the-art. Such a system demonstrates the possibility of using a single, unified architecture for both text detection and recognition with minimal need for elaborate hand-engineering of features.