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2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

19-21 Oct. 2015

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Displaying Results 1 - 25 of 136
  • [Copyright notice]

    Publication Year: 2015, Page(s): 1
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  • [Copyright notice]

    Publication Year: 2015, Page(s): II
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  • Welcome from IEEE DSAA'2015 chairs

    Publication Year: 2015, Page(s):III - IV
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  • Organizing committee

    Publication Year: 2015, Page(s):V - VI
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  • Program Committee

    Publication Year: 2015, Page(s):VII - X
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  • Toward personal knowledge bases

    Publication Year: 2015, Page(s):XI - XVI
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  • Special session on trends & controversies in data science (TCDS)

    Publication Year: 2015, Page(s):XVII - XXVIII
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  • The sexy job in the next ten years will be statisticians

    Publication Year: 2015, Page(s):XXIX - XXXVI
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  • Tutorial 1: NVIDIA's platform for Deep Neural Networks

    Publication Year: 2015, Page(s):XXXVII - XXXIX
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (147 KB) | HTML iconHTML

    Summary form only given. In that session, we will present the NVIDIA's platform for Deep Neural Networks (DNN). NVIDIA designs and produces GPUs and processors that are at the origin of breakthroughs in Deep Learning. NVIDIA also develops CuDNN, a library of primitives for Deep Learning which is integrated in leading frameworks like Theano, Torch and Caffe and DIGITS, an interactive Deep Learning ... View full abstract»

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  • Technical papers

    Publication Year: 2015, Page(s):XL - XLIX
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  • Table of contents

    Publication Year: 2015, Page(s):1 - 10
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  • Matrix factorization approach to behavioral mode analysis from acceleration data

    Publication Year: 2015, Page(s):1 - 6
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (254 KB) | HTML iconHTML

    The field of Movement Ecology is experiencing a period of rapid growth in availability of data, and like many other fields is turning to data science for tools and methods to cope with the new challenges and opportunities that this presents. One rich and interesting source of data is the bio-logger. These small electronic devices are attached to animals free to roam in their natural habitats, and ... View full abstract»

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  • Random-shapelet: An algorithm for fast shapelet discovery

    Publication Year: 2015, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2275 KB) | HTML iconHTML

    Time series shapelets proposes an approach to extract subsequences most suitable to discriminate time series belonging to distinct classes. Computational complexity is the major issue with shapelets: the time required to identify interesting subsequences can be intractable for large cases. In fact, it is required to evaluate all the subsequences of all the time series of the training dataset. In t... View full abstract»

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  • On-line detection of continuous changes in stochastic processes

    Publication Year: 2015, Page(s):1 - 9
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (580 KB) | HTML iconHTML

    This paper addresses the issue of detecting changes in stochastic processes. In conventional studies on change detection, it has been explored how to detect discrete changes for which the statistical models of data suddenly change. We are rather concerned with how to detect continuous changes which occurs incrementally over some successive periods. This paper gives a novel methodology for detectin... View full abstract»

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  • Duration models for activity recognition and prediction in buildings using Hidden Markov Models

    Publication Year: 2015, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (579 KB) | HTML iconHTML

    Activity recognition and prediction in buildings can have multiple positive effects in buildings: improve elderly monitoring, detect intrusions, maximize energy savings and optimize occupant comfort. In this paper we apply human activity recognition by using data coming from a network of motion and door sensors distributed in a Smart Home environment. We use Hidden Markov Models (HMM) as the basis... View full abstract»

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  • Temporal Needleman-Wunsch

    Publication Year: 2015, Page(s):1 - 9
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2558 KB) | HTML iconHTML

    The Needleman-Wunsch algorithm (NW) marked the genesis of a new field of research known as sequence alignment. Its inception was motivated by the growing need for automated methods to find homologous biological sequences. Subsequently, sequence alignment has established itself as a standard approach in bioinformatics, and has also been applied to other domains, including sequences of temporal even... View full abstract»

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  • Exploiting big data in time series forecasting: A cross-sectional approach

    Publication Year: 2015, Page(s):1 - 10
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (250 KB) | HTML iconHTML

    Forecasting time series data is an integral component for management, planning and decision making. Following the Big Data trend, large amounts of time series data are available from many heterogeneous data sources in more and more applications domains. The highly dynamic and often fluctuating character of these domains in combination with the logistic problems of collecting such data from a varie... View full abstract»

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  • Compression rate distance measure for time series

    Publication Year: 2015, Page(s):1 - 10
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2298 KB) | HTML iconHTML

    In this work, we propose a Compression Rate Distance, a new distance measure for time series data. The main idea behind this distance is based on the Minimum Description Length (MDL) principle. The higher compression rate between two time series is, the closer they should be. Besides, we also propose a relaxed version of the new distance, called the Extended Compression Rate Distance. The Extended... View full abstract»

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  • Multi-class learning using data driven ECOC with deep search and re-balancing

    Publication Year: 2015, Page(s):1 - 10
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (253 KB) | HTML iconHTML

    Multi-class learning is an important task in Data Science. One of the ways to achieve good performance on this task is to use Error Correcting Output Codes (ECOC), which is a powerful ensemble learning method that transforms a multi-class problem into a series of binary classifiers which it uses indirectly to learn the original multi-class problem. A crucial component of ECOC is the design of the ... View full abstract»

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  • Calibration of One-Class SVM for MV set estimation

    Publication Year: 2015, Page(s):1 - 9
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (506 KB) | HTML iconHTML

    A general approach for anomaly detection or novelty detection consists in estimating high density regions or Minimum Volume (MV) sets. The One-Class Support Vector Machine (OCSVM) is a state-of-the-art algorithm for estimating such regions from high dimensional data. Yet it suffers from practical limitations. When applied to a limited number of samples it can lead to poor performance even when pic... View full abstract»

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  • Improved risk predictions via sparse imputation of patient conditions in electronic medical records

    Publication Year: 2015, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1534 KB) | HTML iconHTML

    Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key ... View full abstract»

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  • Label noise correction methods

    Publication Year: 2015, Page(s):1 - 9
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (578 KB) | HTML iconHTML

    The important task of correcting label noise is addressed infrequently in literature. The difficulty of developing a robust label correction algorithm leads to this silence concerning label correction. To break the silence, we propose two algorithms to correct label noise. One utilizes self-training to re-label noise, called Self-Training Correction (STC). Another is a clustering-based method, whi... View full abstract»

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  • Hierarchical label partitioning for large scale classification

    Publication Year: 2015, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (303 KB) | HTML iconHTML

    Extreme classification task where the number of classes is very large has received important focus over the last decade. Usual efficient multi-class classification approaches have not been designed to deal with such large number of classes. A particular issue in the context of large scale problems concerns the computational classification complexity : best multi-class approaches have generally a l... View full abstract»

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  • Detecting human emotion via speech recognition by using speech spectrogram

    Publication Year: 2015, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4067 KB) | HTML iconHTML

    This research presents a novel algorithm for detecting human emotion via speech recognition by using speech spectrogram. The proposed algorithm aims to detect the emotional by using information inside the spectrogram. Neural network was used for being the classifier. A new approach to feature extraction based on analysis of two dimensions time-frequency representation of a speech signal have been ... View full abstract»

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  • An effective and economic bi-level approach to ranking and rating spam detection

    Publication Year: 2015, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1051 KB) | HTML iconHTML

    Rating and ranking of items are important parts of modern electronic commerce. As a result, dishonest business owners are spamming the ecosystems in return for favorable product rankings, while consumers can be misled to purchase low quality products. To protect the interests of consumers, it is a critical task to spot spamming activities and maintain the ecosystems health. Existing spam detection... View full abstract»

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