Incremental Adaptive Learning Vector Quantization for Character Recognition with Continuous Style Adaptation

Shen, Yuanyuan; Liu, Cheng‐Lin

doi:10.1007/s12559-017-9491-3

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…The LVQ network proposed on the basis of competitive network structure is a supervised self-organizing neural network [24,25]. In the process of network learning, the supervised signals are added as the classification information to fine-tune the weights, and the output neurons are pre-specified.…”

Section: Learning Vector Quantizationmentioning

confidence: 99%

Distinguish Fritillaria cirrhosa and non-Fritillaria cirrhosa using laser-induced breakdown spectroscopy

Wei

Cui

Teng

et al. 2021

Plasma Sci. Technol.

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As traditional Chinese medicines, Fritillaria from different origins are very similar and it is difficult to distinguish them. In this study, the laser-induced breakdown spectroscopy combined with learning vector quantization (LIBS-LVQ) was proposed to distinguish the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa. We also studied the performance of linear discriminant analysis, and support vector machine on the same data set. Among these three classifiers, LVQ had the highest correct classification rate of 99.17%. The experimental results demonstrated that the LIBS-LVQ model could be used to differentiate the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.

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Section: Learning Vector Quantizationmentioning

confidence: 99%

Distinguish Fritillaria cirrhosa and non-Fritillaria cirrhosa using laser-induced breakdown spectroscopy

Wei

Cui

Teng

et al. 2021

Plasma Sci. Technol.

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Classification of Modi Lipi Characters Using Deep Learning Approach

Chandankhede,

Sachdeo

2024

Lecture Notes in Networks and Systems

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PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams

et al. 2019

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Hot spot identification is a very relevant problem in a wide variety of areas such as health care, energy or transportation. A hot spot is defined as a region of high likelihood of occurrence of a particular event. To identify hot spots, location data for those events is required, which is typically collected by telematics devices. These sensors are constantly gathering information, generating very large volumes of data. Current state-of-the-art solutions are capable of identifying hot spots from big static batches of data by means of variations of clustering or instance selection techniques that pre-process the original input data, providing the most relevant locations. However, these approaches neglect to address changes in hot spots over time.Method: This paper presents a dynamic bio-inspired approach to detect hot spots in big data streams. This computational intelligence method is designed and applied to the transportation sector as a case study to identify incidents in the roads caused by heavy goods vehicles. We adapt an immune-based algorithm to account for the temporary aspect of hot spots inspired by the idea of pheromones, which is then subsequently implemented using Apache Spark Streaming.Results: Experimental results on real datasets with up to 4.5 million data points -provided by a telematics company -show that the algorithm is capable of quickly processing large streaming batches of data, as well as successfully adapting over time to detect hot spots.

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Incremental Adaptive Learning Vector Quantization for Character Recognition with Continuous Style Adaptation

Cited by 5 publications

References 31 publications

Distinguish Fritillaria cirrhosa and non-Fritillaria cirrhosa using laser-induced breakdown spectroscopy

Distinguish Fritillaria cirrhosa and non-Fritillaria cirrhosa using laser-induced breakdown spectroscopy

Classification of Modi Lipi Characters Using Deep Learning Approach

PAS3-HSID: a Dynamic Bio-Inspired Approach for Real-Time Hot Spot Identification in Data Streams

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