A Brief Review of Nearest Neighbor Algorithm for Learning and Classification

Taunk, Kashvi; De, Sanjukta; Verma, Seema; Swetapadma, Aleena

doi:10.1109/iccs45141.2019.9065747

Cited by 496 publications

(202 citation statements)

References 3 publications

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“…Selection of a relevant kernel to characterise the dataset and the run time for large dataset is a challenge. [31]. Effective distance between the dataset and the training sample is computed.…”

Section: Figure 17 Classification Of the Techniques For Vehicle And On-road Object Detectionmentioning

confidence: 99%

Adaptive Headlamps in Automobile: A Review on the Models, Detection Techniques, and Mathematical Models

Toney

Bhargava

2021

IEEE Access

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Driving at night with traditional headlamps poses great threats with a large number of accidents occurring during the night because of temporary blindness caused by the headlights of the oncoming traffic. The headlights when in high beam cause temporary visual impairment of human eyes called the Troxler effect. While it reduces the time to react, it also leads to decreased visibility which contributes to the majority of mishaps that occur at night. Customarily the headlight adjustments are controlled manually where poor driving skills or error in judgment can have catastrophic effects. Accidents also occur due to poor lighting conditions as the current regular headlamp configurations don't illuminate the roads precisely, especially during curves and on unpredictable terrains. Hence there is a need for adaptive headlamps in automobiles that can prevent Troxler's effect on the drivers of the opposite vehicles while not compromising on the illumination of the road for the driver on-board. This paper explores various methodologies used in implementing adaptive headlamps and explore the scope for future work in this area of research. Also, the paper reviews vehicle detection algorithms as well as various vehicle mathematical models for headlamp control based on steering angles INDEX TERMS Adaptive Front light Systems (AFS)

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Section: Figure 17 Classification Of the Techniques For Vehicle And On-road Object Detectionmentioning

confidence: 99%

Adaptive Headlamps in Automobile: A Review on the Models, Detection Techniques, and Mathematical Models

Toney

Bhargava

2021

IEEE Access

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“…Here, Optimal hyperplane is the optimal decision planes that divide the class boundaries with subject to: y n (w In these experiments, the SVM classifier was utilized with a varying kernel function: linear, polynomial, and radial basis function (RBF). The kNN [49] method is straightforward to understand and calculate. It is also a lazy algorithm because it does not involve a training phase.…”

Section: ) Support Vector Machinementioning

confidence: 99%

“…kNN using conventional features: Increasing the k values did not yield important changes to detection accuracy due to the distribution of similarity of training images. Accordingly, the distance of k-nearest neighbors algorithm is a majority vote close to a group of training images[49]. MLP using conventional features: It requires optimizing the suitable number of nodes in hidden layer relating to type of feature and number of input features.…”

mentioning

confidence: 99%

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

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Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.

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“…• k-nearest neighbor (k-NN) [29] is a non-parametric lazy-learner algorithm used for classification and regression. k-NN stores all the available dataset and classifies a new sample based on the feature similarity between this new case and the available data, assigning the most numerous class of the nearest k labeled points.…”

Section: Supervised Learningmentioning

confidence: 99%

“…The k-means method is efficient with guaranteed convergence, and fast when running on small processors with low capabilities [26]. k-NN is a simple algorithm with good performance and requires no training [29]. Finally, DTs require little to no data preprocessing effort and can effectively handle missing values in the data [30].…”

Section: Supervised Learningmentioning

confidence: 99%

Self-Learning Pipeline for Low-Energy Resource-Constrained Devices

et al. 2021

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The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and challenging to annotate. This paper introduces the self-learning autonomous edge learning and inferencing pipeline (AEP), deployable in a resource-constrained embedded system, which can be used for unsupervised local training and classification. AEP uses two complementary approaches: pseudo-label generation with a confidence measure using k-means clustering and periodic training of one of the supported classifiers, namely decision tree (DT) and k-nearest neighbor (k-NN), exploiting the pseudo-labels. We tested the proposed system on two IoT datasets. The AEP, running on the STM NUCLEO-H743ZI2 microcontroller, achieves comparable accuracy levels as same-type models trained on actual labels. The paper makes an in-depth performance analysis of the system, particularly addressing the limited memory footprint of embedded devices and the need to support remote training robustness.

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A Brief Review of Nearest Neighbor Algorithm for Learning and Classification

Cited by 496 publications

References 3 publications

Adaptive Headlamps in Automobile: A Review on the Models, Detection Techniques, and Mathematical Models

Adaptive Headlamps in Automobile: A Review on the Models, Detection Techniques, and Mathematical Models

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Self-Learning Pipeline for Low-Energy Resource-Constrained Devices

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