Machine Learning Technique

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The Experts below are selected from a list of 95487 Experts worldwide ranked by ideXlab platform

Elsayed Z. Soliman - One of the best experts on this subject based on the ideXlab platform.

Christopher J Burke - One of the best experts on this subject based on the ideXlab platform.

  • a Machine Learning Technique to identify transit shaped signals
    The Astrophysical Journal, 2015
    Co-Authors: Susan E Thompson, Fergal Mullally, Jeffrey L Coughlin, Jessie L Christiansen, Christopher E Henze, Michael R Haas, Christopher J Burke
    Abstract:

    We describe a new metric that uses Machine Learning to determine if a periodic signal found in a photometric time series appears to be shaped like the signature of a transiting exoplanet. This metric uses dimensionality reduction and k-nearest neighbors to determine whether a given signal is sufficiently similar to known transits in the same data set. This metric is being used by the Kepler Robovetter to determine which signals should be part of the Q1–Q17 DR24 catalog of planetary candidates. The Kepler Mission reports roughly 20,000 potential transiting signals with each run of its pipeline, yet only a few thousand appear to be sufficiently transit shaped to be part of the catalog. The other signals tend to be variable stars and instrumental noise. With this metric, we are able to remove more than 90% of the non-transiting signals while retaining more than 99% of the known planet candidates. When tested with injected transits, less than 1% are lost. This metric will enable the Kepler mission and future missions looking for transiting planets to rapidly and consistently find the best planetary candidates for follow-up and cataloging.

  • a Machine Learning Technique to identify transit shaped signals
    arXiv: Earth and Planetary Astrophysics, 2015
    Co-Authors: Susan E Thompson, Fergal Mullally, Jeffrey L Coughlin, Jessie L Christiansen, Christopher E Henze, Michael R Haas, Christopher J Burke
    Abstract:

    We describe a new metric that uses Machine Learning to determine if a periodic signal found in a photometric time series appears to be shaped like the signature of a transiting exoplanet. This metric uses dimensionality reduction and k-nearest neighbors to determine whether a given signal is sufficiently similar to known transits in the same data set. This metric is being used by the Kepler Robovetter to determine which signals should be part of the Q1-Q17 DR24 catalog of planetary candidates. The Kepler Mission reports roughly 20,000 potential transiting signals with each run of its pipeline, yet only a few thousand appear sufficiently transit shaped to be part of the catalog. The other signals tend to be variable stars and instrumental noise. With this metric we are able to remove more than 90% of the non-transiting signals while retaining more than 99% of the known planet candidates. When tested with injected transits, less than 1% are lost. This metric will enable the Kepler mission and future missions looking for transiting planets to rapidly and consistently find the best planetary candidates for follow-up and cataloging.

Rodney Sparapani - One of the best experts on this subject based on the ideXlab platform.

Gavin Finnie - One of the best experts on this subject based on the ideXlab platform.

  • financial time series forecasting with Machine Learning Techniques a survey
    The European Symposium on Artificial Neural Networks, 2010
    Co-Authors: Bjoern Krollner, Bruce J Vanstone, Gavin Finnie
    Abstract:

    Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of Machine Learning Techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to the Machine Learning Technique used, the forecasting timeframe, the input variables used, and the evaluation Techniques employed. It is found that there is a consensus between researchers stressing the importance of stock index forecasting. Artificial Neural Networks (ANNs) are identified to be the dominant Machine Learning Technique in this area. We conclude with possible future research directions.

  • ESANN - Financial time series forecasting with Machine Learning Techniques: A survey
    2010
    Co-Authors: Bjoern Krollner, Bruce J Vanstone, Gavin Finnie
    Abstract:

    Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of Machine Learning Techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to the Machine Learning Technique used, the forecasting timeframe, the input variables used, and the evaluation Techniques employed. It is found that there is a consensus between researchers stressing the importance of stock index forecasting. Artificial Neural Networks (ANNs) are identified to be the dominant Machine Learning Technique in this area. We conclude with possible future research directions.

Mattia Venditti - One of the best experts on this subject based on the ideXlab platform.

  • an unsupervised Machine Learning Technique for the definition of a rule based control strategy in a complex hev
    SAE International Journal of Alternative Powertrains, 2016
    Co-Authors: Roberto Finesso, Ezio Spessa, Mattia Venditti
    Abstract:

    An unsupervised Machine-Learning Technique, aimed at the identification of the optimal rule-based control strategy, has been developed for parallel hybrid electric vehicles that feature a torque-coupling (TC) device, a speed-coupling (SC) device or a dual-mode system, which is able to realize both actions. The approach is based on the preliminary identification of the optimal control strategy, which is carried out by means of a benchmark optimizer, based on the deterministic dynamic programming Technique, for different driving scenarios. The optimization is carried out by selecting the optimal values of the control variables (i.e., transmission gear and power flow) in order to minimize fuel consumption, while taking into account several constraints in terms of NOx emissions, battery state of charge and battery life consumption. The results of the benchmark optimizer are then processed with the aim of extracting a set of optimal rule-based control strategies, which can be implemented onboard in real-time. The input variables of the rule-based strategy are the vehicle power demand, the vehicle speed and the state of charge of the battery. The method for the rule extraction can be summarized as follows. A clustering algorithm discretizes the input domain (in terms of vehicle power demand, vehicle speed and state of charge of the battery) into a mesh of clusters. The generic rule associated to a specific cluster (i.e., the combination of gear and power flow that has to be actuated) is identified by searching for the control strategy most frequently adopted by the benchmark optimizer within the considered cluster. The optimal mesh of clusters is generated using a genetic algorithm Technique. Optimal sets of rules are identified for different driving scenarios. These strategies can then be implemented on-board, provided the mission features are known at the beginning of the trip. The main advantage of the proposed Technique is that the definition of the rule-based strategy is derived from a Machine Learning method and is not based on heuristic Techniques.

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