Adaptive learning in the presence of noise


Adaptive learning in the presence of noise – The problem of learning from high-dimensional data is studied in the context of probabilistic inference, which in turn involves learning probability distributions from large numbers of items. This task can be considered as the problem of learning from a sparse representation of an input, and with a high probability in the direction of inference, in order to achieve high inference accuracy. Despite this fact, low-dimensional data often exhibit high probability in the direction of inference, which indicates that a learning problem can have a high-confidence bias. In this paper, we propose a deep learning algorithm to learn a Bayesian inference problem from both a very sparse representation of an input and the posterior distribution of the input. Our work has been validated on several datasets and we show that it improves performance of our algorithm by reducing the number of labeled items by a factor of up to ~1x-$O$.

This paper presents an approach to segment and classify human action recognition tasks. Motivated by human action and visual recognition we use an ensemble of three human action recognition tasks to classify action images and use an explicit representation of their input labels. Based on a new metric used to classify action images, we propose to use an ensemble of visual tracking models (e.g. the multi-view or multi-label approach) to classify the recognition tasks. Our visual tracking model aims at maximizing the information flow between visual and non-visual features, which allows for better segmentation and classification accuracy. We evaluate our approach using a dataset of over 30,000 labeled action images from various action recognition tasks and compare to state-of-the-art segmentation and classification performance, using an analysis of the visual recognition task. Our method consistently outperforms the state-of-the-art on both tasks.

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Adaptive learning in the presence of noise

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  • Robustness of Fuzzy Modeling and Its Applications in Clustering and Classification Problems

    Learning from Imprecise Measurements by Transferring Knowledge to An Explicit ClassifierThis paper presents an approach to segment and classify human action recognition tasks. Motivated by human action and visual recognition we use an ensemble of three human action recognition tasks to classify action images and use an explicit representation of their input labels. Based on a new metric used to classify action images, we propose to use an ensemble of visual tracking models (e.g. the multi-view or multi-label approach) to classify the recognition tasks. Our visual tracking model aims at maximizing the information flow between visual and non-visual features, which allows for better segmentation and classification accuracy. We evaluate our approach using a dataset of over 30,000 labeled action images from various action recognition tasks and compare to state-of-the-art segmentation and classification performance, using an analysis of the visual recognition task. Our method consistently outperforms the state-of-the-art on both tasks.


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