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Found 15 papers, 0 papers with code
ADABOOK & MULTIBOOK: Adaptive Boosting with Chance Correction
There has been considerable interest in boosting and bagging, including the combination of the adaptive techniques of AdaBoost with the random selection with replacement techniques of Bagging.
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic.
DREAMT -- Embodied Motivational Conversational Storytelling
Storytelling is fundamental to language, including culture, conversation and communication in their broadest senses.
Informing Computer Vision with Optical Illusions
Illusions are fascinating and immediately catch people's attention and interest, but they are also valuable in terms of giving us insights into human cognition and perception.
Prediction of Dashed Café Wall illusion by the Classical Receptive Field Model
We demonstrate that a simple Differences of Gaussian model provides an explanatory mechanism for the illusory tilt perceived in a family of Caf\'e Wall illusion generalizes to the dashed versions of Caf\'e Wall.
The Cafe Wall Illusion: Local and Global Perception from multiple scale to multiscale
We present here a precise and quantified comparison contrasting local tilt detection in the foveal sets with a global average across all of the Caf\'e Wall configurations tested in this work.
A Predictive Account of Cafe Wall Illusions Using a Quantitative Model
This paper explores the tilt illusion effect in the Cafe Wall pattern using a classical Gaussian Receptive Field model.
Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual grouping
The output of our low-level filtering model is presented for several types of Tilt Illusion, predicting that the final tilt percept arises from multiple-scale processing of the Differences of Gaussians and the perceptual interaction of foreground and background elements.
An Integer Programming Model for Binary Knapsack Problem with Value-Related Dependencies among Elements
Binary Knapsack Problem (BKP) is to select a subset of an element (item) set with the highest value while keeping the total weight within the capacity of the knapsack.
Characterisation of speech diversity using self-organising maps
We report investigations into speaker classification of larger quantities of unlabelled speech data using small sets of manually phonemically annotated speech.
A quantitative analysis of tilt in the Café Wall illusion: a bioplausible model for foveal and peripheral vision
The biological characteristics of human visual processing can be investigated through the study of optical illusions and their perception, giving rise to intuitions that may improve computer vision to match human performance.
Throwing fuel on the embers: Probability or Dichotomy, Cognitive or Linguistic?
Prof. Robert Berwick's abstract for his forthcoming invited talk at the ACL2016 workshop on Cognitive Aspects of Computational Language Learning revives an ancient debate.
Visualization of Tradeoff in Evaluation: from Precision-Recall & PN to LIFT, ROC & BIRD
Evaluation often aims to reduce the correctness or error characteristics of a system down to a single number, but that always involves trade-offs.
Evaluation Evaluation a Monte Carlo study
Over the last decade there has been increasing concern about the biases embodied in traditional evaluation methods for Natural Language Processing/Learning, particularly methods borrowed from Information Retrieval.
What the F-measure doesn't measure: Features, Flaws, Fallacies and Fixes
The F-measure or F-score is one of the most commonly used single number measures in Information Retrieval, Natural Language Processing and Machine Learning, but it is based on a mistake, and the flawed assumptions render it unsuitable for use in most contexts!
