Scattering Transforms (or ScatterNets) introduced by Mallat in [1] are a promising start into creating a well-defined feature extractor to use for pattern recognition and image classification tasks. They are of partic...
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For the Fourth Annual 2008machinelearning for signalprocessing competition entrants were asked to develop a machinelearning algorithm that maximizes the rate of return by trading (buying, selling, shorting, or cov...
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ISBN:
(纸本)9781424423750
For the Fourth Annual 2008machinelearning for signalprocessing competition entrants were asked to develop a machinelearning algorithm that maximizes the rate of return by trading (buying, selling, shorting, or covering) stocks over a six-month time period. Each entrant began with a (fictional) $100,000 USD. Both the training and the test set include the daily price and volume for a total of 2929 stocks that are traded in American stock markets and a total of 41 monthly indices. Stock valuations are based on real (historical) stock prices. This year there were 5 algorithms submitted. The highest annual rate of return of an astonishing 150% was obtained by Peng and Ji of the Rensselaer Polytechnic Institute/Shanghai Maritime University team.
In this paper, we propose a new learning framework based on the mathematical concept of varifolds [1], which are the measure-theoretic generalization of differentiable manifolds. We compare varifold learning with the ...
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ISBN:
(纸本)9781424423750
In this paper, we propose a new learning framework based on the mathematical concept of varifolds [1], which are the measure-theoretic generalization of differentiable manifolds. We compare varifold learning with the popular manifold learning and demonstrate some of its specialties. Algorithmically, we derive a neighborhood refinement technique for hyperaraph models, which is conceptually analogous to varifolds, give the procedure for constructing such hypergraphs from data and finally by using the hypergraph Laplacian matrix we are able to solve high-dimensional classification problems accurately.
This paper addresses the question of temporal learning in spatially distributed wireless sensor networks (WSN). We propose to fuse WSNs with the Echo States Network learning concepts to infer the spatio-temporal dynam...
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ISBN:
(纸本)9781424423750
This paper addresses the question of temporal learning in spatially distributed wireless sensor networks (WSN). We propose to fuse WSNs with the Echo States Network learning concepts to infer the spatio-temporal dynamics of the data collaboratively measured by sensors. We prove that a WSN topology described by a bidirected graph is strongly connected, which is a sufficient and necessary condition for implementing in-network distributed learning. For strongly connected networks we develop a systematic method to satisfy the conditions resulting in echo states in sensor networks. The effectiveness of the learning approach is demonstrated with several controlled model experiments.
Classification problems in dynamical environments are in many fields, including signalprocessing and pattern recognition. In this paper, we propose a novel Bayesian approach to classification in a dynamical environme...
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ISBN:
(纸本)9781424423750
Classification problems in dynamical environments are in many fields, including signalprocessing and pattern recognition. In this paper, we propose a novel Bayesian approach to classification in a dynamical environment. The proposed approach employs natural sequential prior to improve online learning for an online classifier model. By using the natural sequential prior, the proposed approach describes the dynamical changes in the classifier model's parameters in a more natural manner. For comparison, the proposed approach and a conventional approach are validated by means of several numerical experiments.
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