Self-incremental learning vector quantization with human cognitive biases

Manome, Nobuhito; Shinohara, Shuji; Takahashi, Tatsuji; Chung, Ung‐il

doi:10.1038/s41598-021-83182-4

Cited by 7 publications

(4 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [19] and [20], LVQ is a supervised classification algorithm, which is widely used for classification problems due to its easy implementation and understanding. The purpose of LVQ is to learn a codebook (or prototype) for assigning an arbitrary input vector to a target class label from training data composed of an input vector x and a corresponding label y.…”

Section: Proposed Intelligent Pir System Using Learning Vector Quanti...mentioning

confidence: 99%

Intelligent passive infrared sensor based on learning vector quantization for human detection

Doan,

Nguyen,

Hoang

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

Passive Infrared (PIR) Sensors have been used widely in human detection indoors nowadays due to their low cost and range. However, traditional PIR sensors may get fault detection, especially when the human is in a static pose. To overcome this limitation, a Machine Learning (ML)-based PIR sensor is proposed in this work for detection accuracy enhancement. The Learning Vector Quantization (LVQ) approach is used to be easily implemented in the embedded device (which requires a low computational complexity) to provide a real-time response. The experimental scenarios to create the datasets are conducted in two distinct locations for training and testing purposes. In each location, participants performed a series of different activities and left the room unoccupied. Data is collected via a PIR sensor and then wireless transmitted to a computer for training and testing. In the test set, the presence of humans with an accuracy of 89.25% is obtained using the proposed LVQ algorithm prediction. Finally, the LVQ is implemented on an embedded device based on Xtensa Dual-Core 32-bit LX6 CPU to form an intelligent PIR (iPIR)-based LVQ sensor, this novel iPIR sensor then is evaluated and tested with a remarkable result.

show abstract

Section: Proposed Intelligent Pir System Using Learning Vector Quanti...mentioning

confidence: 99%

Intelligent passive infrared sensor based on learning vector quantization for human detection

Doan,

Nguyen,

Hoang

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…biased. Taniguchi, Sato, and Shirakawa (105) and Manome et al (106) extend this idea by incorporating the same biases in neural networks and learning vector quantization respectively for different tasks.…”

Section: Decisionmentioning

confidence: 99%

BIASeD: Bringing Irrationality into Automated System Design

Gulati¹,

Lozano²,

Lepri³

et al. 2022

Preprint

View full text Add to dashboard Cite

Human perception, memory and decision-making are impacted by tens of cognitive biases and heuristics that influence our actions and decisions. Despite the pervasiveness of such biases, they are generally not leveraged by today's Artificial Intelligence (AI) systems that model human behavior and interact with humans. In this theoretical paper, we claim that the future of human-machine collaboration will entail the development of AI systems that model, understand and possibly replicate human cognitive biases. We propose the need for a research agenda on the interplay between human cognitive biases and Artificial Intelligence. We categorize existing cognitive biases from the perspective of AI systems, identify three broad areas of interest and outline research directions for the design of AI systems that have a better understanding of our own biases.

show abstract

“…Chen and Lee [19] propose an incremental few-shot learning algorithm that makes use of deep incremental learning vector quantization to solve problems related to catastrophic forgetting in classincremental tasks. A self-incremental learning vector quantization algorithm (SILVQ) is proposed in [20];…”

Section: B Learning Vector Quantizationmentioning

confidence: 99%

XuILVQ: A River Implementation of the Incremental Learning Vector Quantization for IoT

Soto

Castro

Redondo

et al. 2022

Proceedings of the 19th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, &Amp; Ubiquitous Netw

View full text Add to dashboard Cite

Decentralised machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralised and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.

show abstract

Self-incremental learning vector quantization with human cognitive biases

Cited by 7 publications

References 51 publications

Intelligent passive infrared sensor based on learning vector quantization for human detection

Intelligent passive infrared sensor based on learning vector quantization for human detection

BIASeD: Bringing Irrationality into Automated System Design

XuILVQ: A River Implementation of the Incremental Learning Vector Quantization for IoT

Contact Info

Product

Resources

About