Learning from Imprecise Measurements by Transferring Knowledge to An Explicit Classifier


Learning from Imprecise Measurements by Transferring Knowledge to An Explicit Classifier – 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.

The problem of estimating a given prediction is a nonlinear and non-parametric phenomenon of high nonlinearity. The classical and recent algorithms are unable to estimate prediction probability as well as prediction probability for the data, and consequently, these algorithms are largely limited to estimating a low-parameter probability distribution. In this paper, we focus on the estimation of the probability of prediction for certain conditions under a particular scenario with high nonlinearity. We propose a principled algorithm which learns Bayes Bayesian Optimality (BBA) using a priori knowledge of the probability of prediction, and we compare the algorithm to Bayesian optimization. Compared to the state of the art, our algorithm outperforms the more traditional optimization method, while outperforming the previous state of the art algorithms in terms of accuracy and time.

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Learning from Imprecise Measurements by Transferring Knowledge to An Explicit Classifier

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    A Fuzzy Rule Based Model for Predicting Performance of Probabilistic Forecasting AgentsThe problem of estimating a given prediction is a nonlinear and non-parametric phenomenon of high nonlinearity. The classical and recent algorithms are unable to estimate prediction probability as well as prediction probability for the data, and consequently, these algorithms are largely limited to estimating a low-parameter probability distribution. In this paper, we focus on the estimation of the probability of prediction for certain conditions under a particular scenario with high nonlinearity. We propose a principled algorithm which learns Bayes Bayesian Optimality (BBA) using a priori knowledge of the probability of prediction, and we compare the algorithm to Bayesian optimization. Compared to the state of the art, our algorithm outperforms the more traditional optimization method, while outperforming the previous state of the art algorithms in terms of accuracy and time.


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