Iterative learning from texts and counterexamples using additional information

Jain, Sanjay; Kinber, Efim

doi:10.1007/s10994-011-5238-7

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…This process reduces the classification error and generates a prediction rule that leads to an improvement of a learned function. In the literature, we can find different examples of iterative learning applications in problems related to text recognition, control, data de-noising and model accuracy improvement [ 9 , 10 , 11 , 12 , 13 , 14 ].…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework

Dávila

Creţu

Zaremba

2017

Sensors

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The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

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Section: Literature Review and Related Workmentioning

confidence: 99%

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework

Dávila

Creţu

Zaremba

2017

Sensors

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“…As it is shown in [JK11] all indexed classes are NCIt-learnable class-preservingly if a learner has access to additional information of certain types.…”

Section: Theorem 26 Every Indexed Class Of Languages Is In Ncitmentioning

confidence: 99%

“…In [JK11], the extensions of NCIt model -using additional information of the following four different types -have been introduced and studied:…”

Section: Theorem 26 Every Indexed Class Of Languages Is In Ncitmentioning

confidence: 99%