Counting Pedestrian with Mixed Features and Extreme Learning Machine

Li, Yuanwei; Zhu, En; Zhu, Xiaoyan; Yin, Jianping; Zhao, Jiwen

doi:10.1007/s12559-014-9248-1

Cited by 17 publications

(2 citation statements)

References 21 publications

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“…Note that N is different for datasets, e.g., according to Moreover, all parameters were optimized based on a validation set of [33][34][35][36][37] cases which were selected in the respective test period (illustrated in Table 2). In the test period, one case was selected in every 10 days to construct the validation dataset and the remaining cases were employed as the test data.…”

Section: Setup Of Re-elm Os-elm and Mcos-elmmentioning

confidence: 99%

Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

Vong

Chiu

et al. 2014

Cogn Comput

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Many time series problems such as air pollution index forecast require online sequential learning rather than batch learning. One of the major obstacles for air pollution index forecast is the data imbalance problem so that forecast model biases to the majority class. This paper proposes a new method called meta-cognitive online sequential extreme learning machine (MCOS-ELM) that aims to alleviate data imbalance problem and sequential learning at the same time. Under a real application of air pollution index forecast, the proposed MCOS-ELM was compared with retrained ELM and online sequential extreme learning machine in terms of accuracy and computational time. Experimental results show that MCOS-ELM has the highest efficiency and best accuracy for predicting the minority class (i.e., the most important but with fewest training samples) of air pollution level.

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Section: Setup Of Re-elm Os-elm and Mcos-elmmentioning

confidence: 99%

Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

Vong

Chiu

et al. 2014

Cogn Comput

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“…The main requirement behind this principle, which yields better results, is that there should be significant differences or diversity among the models. Many examples of the use of this principle in cognitive computation exist in the literature [44][45][46][47][48][49][50]. In accordance with the intrinsic hierarchy present in the data set, we will study two different scenarios: extrapolation with respect to different exercises and violinists.…”

Section: Introductionmentioning

confidence: 99%

Understanding Violin Players’ Skill Level Based on Motion Capture: a Data-Driven Perspective

et al. 2020

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Learning to play and perform a music instrument is a complex cognitive task, requiring high conscious control and coordination of an impressive number of cognitive and sensorimotor skills. For professional violinists, there exists a physical connection with the instrument allowing the player to continuously manage the sound through sophisticated bowing techniques and fine hand movements. Hence, it is not surprising that great importance in violin training is given to right hand techniques, responsible for most of the sound produced. In this paper, our aim is to understand which motion features can be used to efficiently and effectively distinguish a professional performance from that of a student without exploiting sound-based features. We collected and made freely available a dataset consisting of motion capture recordings of different violinists with different skills performing different exercises covering different pedagogical and technical aspects. We then engineered peculiar features and trained a data-driven classifier to distinguish among two different levels of violinist experience, namely beginners and experts. In accordance with the hierarchy present in the dataset, we study two different scenarios: extrapolation with respect to different exercises and violinists. Furthermore, we study which features are the most predictive ones of the quality of a violinist to corroborate the significance of the results. The results, both in terms of accuracy and insight on the cognitive problem, support the proposal and support the use of the proposed technique as a support tool for students to monitor and enhance their home study and practice.

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Unsupervised Crowd Counting

Elassal

Elder

2017

Computer Vision – ACCV 2016

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Counting Pedestrian with Mixed Features and Extreme Learning Machine

Cited by 17 publications

References 21 publications

Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

Understanding Violin Players’ Skill Level Based on Motion Capture: a Data-Driven Perspective

Unsupervised Crowd Counting

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