A Second-Order Perceptron Algorithm

Cesa-Bianchi, Nicolò; Conconi, Alex; Gentile, Claudio

doi:10.1007/3-540-45435-7_9

Cited by 44 publications

(80 citation statements)

References 19 publications

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“…5). To choose the proper OML algorithm, benchmark experiments were carried out with 14 different OML algorithms: Perceptron [15], RAMMA and agg-RAMMA [16], OGD [17], PA- I and PA-II [18], SOP [19], CW [20], IEL-LIP [21], NHERD [22], AROW [23], NAROW [24], SCW-I and SCW-II [10], using 100 interictal discharges and 100 non-interictal discharges from the same record. As shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

SpikeGUI: Software for rapid interictal discharge annotation via template matching and online machine learning

Jin

Dauwels

Cash

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Detection of interictal discharges is a key element of interpreting EEGs during the diagnosis and management of epilepsy. Because interpretation of clinical EEG data is time-intensive and reliant on experts who are in short supply, there is a great need for automated spike detectors. However, attempts to develop general-purpose spike detectors have so far been severely limited by a lack of expert-annotated data. Huge databases of interictal discharges are therefore in great demand for the development of general-purpose detectors. Detailed manual annotation of interictal discharges is time consuming, which severely limits the willingness of experts to participate. To address such problems, a graphical user interface “SpikeGUI” was developed in our work for the purposes of EEG viewing and rapid interictal discharge annotation. “SpikeGUI” substantially speeds up the task of annotating interictal discharges using a custom-built algorithm based on a combination of template matching and online machine learning techniques. While the algorithm is currently tailored to annotation of interictal epileptiform discharges, it can easily be generalized to other waveforms and signal types.

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Section: Resultsmentioning

confidence: 99%

SpikeGUI: Software for rapid interictal discharge annotation via template matching and online machine learning

Jin

Dauwels

Cash

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…2) Second-order algorithms: such Second Order Perceptron (SOP) [15], Confidence Weigted learning (CW) [16], etc.…”

Section: ) First-order Algorithmsmentioning

confidence: 99%

ANOFS: Automated negotiation based online feature selection method

Saïd

Alimi

2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

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Feature selection is an important technique in machine learning and pattern classification. Most existing studies of feature selection are using the batch learning methods. Such methods are not appropriate for real-world applications especially when data arrive sequentially. Recently, this problem is addressed by some feature selection techniques using online learning. Despite the advantages in efficiency of online feature selection methods, they are not always accurate enough whenhandling real world data. In this paper, we address this limitation by the integration of automated negotiation process. We present a novel method based on negotiation theory for online feature selection (ANOFS) and demonstrate its application to several public datasets.

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“…There are several popular online methods such as perceptron (Rosenblatt, 1958), passive-aggressive (Crammer et al, 2006), stochastic gradient descent (Zhang, 2004), aggregating algorithm (Vovk, 2001) and the second order perceptron (Cesa-Bianchi et al, 2005). In (Cesa-Bianchi and Lugosi, 2006), an in-deph analysis of online learning is provided.…”

Section: Introductionmentioning

confidence: 99%

An Online Vector Error Correction Model for Exchange Rates Forecasting

Arce

Antognini

Kristjanpoller

et al. 2015

Proceedings of the International Conference on Pattern Recognition Applications and Methods

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Financial time series are known for their non-stationary behaviour. However, sometimes they exhibit some stationary linear combinations. When this happens, it is said that those time series are cointegrated.The Vector Error Correction Model (VECM) is an econometric model which characterizes the joint dynamic behaviour of a set of cointegrated variables in terms of forces pulling towards equilibrium. In this study, we propose an Online VEC model (OVECM) which optimizes how model parameters are obtained using a sliding window of the most recent data. Our proposal also takes advantage of the long-run relationship between the time series in order to obtain improved execution times. Our proposed method is tested using four foreign exchange rates with a frequency of 1-minute, all related to the USD currency base. OVECM is compared with VECM and ARIMA models in terms of forecasting accuracy and execution times. We show that OVECM outperforms ARIMA forecasting and enables execution time to be reduced considerably while maintaining good accuracy levels compared with VECM.

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A Second-Order Perceptron Algorithm

Cited by 44 publications

References 19 publications

SpikeGUI: Software for rapid interictal discharge annotation via template matching and online machine learning

SpikeGUI: Software for rapid interictal discharge annotation via template matching and online machine learning

ANOFS: Automated negotiation based online feature selection method

An Online Vector Error Correction Model for Exchange Rates Forecasting

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