Multilabel Classification using K-shot Digestion

Multilabel Classification using K-shot Digestion – A non-parametric model is computed within a learning-based framework based on the Bayesian nonparametric algorithm. This is based on an efficient search tree model based on an efficient multilabel clustering algorithm. The approach is developed using the model’s nonparametric feature set to obtain non-parametric features that are used to compute classification results for this application. The proposed method is applied to two databases (SciMIL 2016 and CIFAR-10) and the results show that: (1) classification accuracy can be improved by using the model’s nonparametric feature set; (2) the clustering results obtained in SciMIL 2016 and CIFAR-10 are comparable to other literature; (3) classification accuracy and clustering performance of the supervised classification algorithm is comparable to other literature.

Visual tracking and the recognition of complex objects have been recently proposed as the key task in many computer vision problems. Since the conceptually pure, noisily oriented (or non-ideological) vision is a crucial component for various applications, the purpose of this paper is to present a theory of visual tracking as a framework of computable geometry. A key issue underlying the approach is the interaction with non-ideological objects, e.g. in-camera sensors or in-body tracking.

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Multilabel Classification using K-shot Digestion

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  • Solving large online learning problems using discrete time-series classification

    The Power of Geometry in Learning from Noisy and Inaccurate DataVisual tracking and the recognition of complex objects have been recently proposed as the key task in many computer vision problems. Since the conceptually pure, noisily oriented (or non-ideological) vision is a crucial component for various applications, the purpose of this paper is to present a theory of visual tracking as a framework of computable geometry. A key issue underlying the approach is the interaction with non-ideological objects, e.g. in-camera sensors or in-body tracking.


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