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نتیجه جستجو - ماشین های آموزش عالی

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Train Delay Prediction Systems: A Big Data Analytics Perspective
سیستم پیش بینی تأخیر در قطار: چشم انداز تجزیه و تحلیل داده بزرگ-2017
Current train delay prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of historical train movements data collected by the railway information systems. Instead, they rely on static rules built by experts of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven Train Delay Prediction System (TDPS) for large-scale railway networks which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.
Keywords: Railway network | Train Delay Prediction systems | Big data analytics | Extreme learning machines | Shallow architecture | Deep architecture
مقاله انگلیسی
2 Ensemble of Extreme Learning Machines with Trained Classifier Combination and Statistical Features for Hyperspectral Data
اثر کلی از ماشین های یادگیری وسیع با ترکیب مرتب آموزش یافته و ویژگی های آماری برای داده های Hyperspectral-2017
Remote sensing and hyperspectral data analysis are areas offering wide range of valuable practical applications. However, they generate massive and complex data that is very difficult to be analyzed by a human being. Therefore, methods for efficient data representation and data mining are of high interest to these fields. In this paper we introduce a novel pipeline for feature extraction and classification of hyperspectral images. To obtain a compressed representation we propose to extract a set of statistical-based properties from these images. This allows for embedding feature space into fourteen channels, obtaining a significant dimensionality reduction. These features are used as an input for the ensemble learning based on randomized neural networks. We introduce a novel method for forming ensembles of extreme learning machines based on randomized feature subspaces and a trained combiner. It is based on continuous outputs and uses a perceptron- based learning scheme to calculate weights assigned to each classifier and class independently. Extensive experiments carried on a number of benchmarks images prove that using proposed feature extraction and extreme learning ensemble leads to a significant gain in classification accuracy.
Keywords: Ensemble learning | Extreme learning machines | Hyperspectral imaging | Computer vision | Feature extraction | Dimensionality reduction
مقاله انگلیسی
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