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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是21-30 订阅

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Tight Lower Bound of Generalization Error in Ensemble learning 7

Tight Lower Bound of Generalization Error in Ensemble Learni...

引用

Joint 7th international conference on soft computing and Intelligent Systems (SCIS) and 15th international Symposium on Advanced Intelligent Systems (ISIS)

作者： Uchida, Masato Chiba Inst Technol Fac Engn Narashino Chiba 2750016 Japan

ISBN: (纸本)9781479959556

A machine learning method that integrates multiple component predictors into one predictor is referred to as ensemble learning. In this paper, we derive a tight lower bound of the generalization error in ensemble learning based on asymptotic analysis using a mathematical model based on an exponential mixture of probability distributions and the Kullback-Leibler divergence. In addition, we derive the explicit expression of the weight parameter used in the exponential mixture of probability distributions that minimizes the tight lower bound and discuss the property of the derived weight parameter.

关键词： ensemble learning parameter estimation exponential mixture model generalization error asymptotic analysis

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Energy Efficient Hyperparameters Tuning through Augmented Gaussian Processes and Multi-information Source Optimization 7

Energy Efficient Hyperparameters Tuning through Augmented Ga...

引用

7th international conference on soft computing and machine Intelligence (ISCMI)

作者： Candelieri, Antonio Archetti, Francesco Ponti, Andrea Perego, Riccardo Univ Milano Bicocca Dept Econ Management & Stat Milan Italy Univ Milano Bicocca Dept Comp Sci Syst & Commun Milan Italy

ISBN: (纸本)9781728175591

Searching for the optimal values of hyperparameters of a machine learning algorithm can be an extremely computationally expensive and awfully energivorous process. this paper proposes multi information source optimization as a strategy to drastically reduce computational time and energy consumed by this search process, where the information sources, with different computational costs, are smaller portions of a large dataset. the algorithm fits into the scheme of Gaussian Process based Bayesian Optimization: an Augmented Gaussian Process is proposed, which is trained using only "reliable" information among available sources. A novel acquisition function is also defined according to the Augmented Gaussian Process. Results on the hyperparameter optimization of a Support Vector machine for a classification task on a large dataset are presented, with a comparison in terms of the misclassification error, on 10 fold-cross validation, and computational cost between the proposed approach and traditional hyperparameter optimization using the entire dataset.

关键词： green AI green machine learning multi information source optimization hayesian optimization gaussian processes

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Surrogate Assisted Methods for the Parameterisation of Agent-Based Models 7

Surrogate Assisted Methods for the Parameterisation of Agent...

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7th international conference on soft computing and machine Intelligence (ISCMI)

作者： Perumal, Rylan van Zyl, Terence L. Univ Witwatersrand Sch Comp Sci & Appl Math Johannesburg South Africa Univ Johannesburg Inst Intelligent Syst Johannesburg South Africa

ISBN: (纸本)9781728175591

Parameter calibration is a major challenge in agent-based modelling and simulation (ABMS). As the complexity of agent-based models (ABMs) increase, the number of parameters required to he calibrated grows. this leads to the ABMS equivalent of the "curse of dimensionality". We propose an ABMS framework which facilitates the effective integration of different sampling methods and surrogate models (SMs) in order to evaluate how these strategies affect parameter calibration and exploration. We show that surrogate assisted methods perform better than the standard sampling methods. In addition, we show that the XGBoost and Decision Tree SMs are most optimal overall with regards to our analysis.

关键词： Agent-based modelling and simulation surrogate models infectious disease epidemiology machine learning

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A Hybrid machine learning Method for Diabetes Detection based on Unsupervised Clustering 23

A Hybrid Machine Learning Method for Diabetes Detection base...

引用

7th international conference on machine learning and soft computing, icmlsc 2023

作者： Liu, Junhong Peng, Bo Yin, Zezhao School of Computer and Artificial Intelligence Southwest Jiaotong University China

ISBN: (纸本)9781450398633

Diabetes is a common disease, and due to the increasing incidence year by year. But most diabetics can not be easily detected in the early stage, since the symptoms are not obvious. the objective of this study is to propose a machine-learning method based on unsupervised clustering to improve the accuracy of diabetes detection. Due to massive unlabeled data sets and the problems in the traditional K-means clustering algorithms, we adopt the Fuzzy c-means clustering algorithm with an improvement on the calculation of parameter m. Our method includes a combination of the principal component analysis(PCA), an improved Fuzzy c-means (FCM) clustering algorithm, and K-nearest neighbor(KNN) classification algorithm optimized with K value. After 10 times 10-fold cross-validation, the average accuracy of the proposed method reaches 99.31%, which is higher than that of other machine learning models. therefore, our method is proven to be more suitable for detecting diabetes. At the same time, further experiments on a new data set validate the applicability of our method in a more practical way for the diabetes detection. © 2023 ACM.

关键词： Principal component analysis

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Automated Identification of Individuals in Wildlife Population Using Siamese Neural Networks 7

Automated Identification of Individuals in Wildlife Populati...

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7th international conference on soft computing and machine Intelligence (ISCMI)

作者： Dlamini, Nkosikhona van Zyl, Terence L. Univ Witwatersrand Sch Comp Sci & Appl Math Johannesburg South Africa Univ Johannesburg Inst Intelligent Syst Johannesburg South Africa

ISBN: (纸本)9781728175591

Similarity learning coupled with semi-hard pair mining has been applied successfully in human individual identification using images of faces. this approach is coupled with innovative training data sampling techniques, trained to optimise a ranking loss function, aimed at increasing model performance at a minimal additional computational cost. We demonstrate that similarity learning coupled with semi-hard negative pair mining. minimising a triplet loss function, can be applied in the identification of wild animals: Lions, Zebra, Nyalas, and Chimpanzees. there is varying performance depending on the dataset being studied and the network architecture. there is improved performance on models trained using semi-hard triplets on the Chimpanzees hold out test-set data;VGG-19 achieves a 96% accuracy and DenseNet-201 90.1% accuracy. Mean average precision was measured for the different network architectures, varying performances were obtained depending on dataset and network depth.

关键词： wildlife siamese neural networks triplet-loss hard negative mining semi-hard negative mining transfare-learing similarity learning

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Post Processing Selection of Automatic Item Generation in Testing to Ensure Human-Like Quality with machine learning 23

Post Processing Selection of Automatic Item Generation in Te...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Duvvuri, Venkata Lee, Gahyoung Hsu, Yuwei Makwana, Asha Morgan, Chris College of Professional Studies Northeastern University United States AITestBanks Inc. United States

ISBN: (纸本)9781450398633

Automatic Item Generation (AIG) is increasingly used to process large amounts of information and scale the demand for computerized testing. Recent work in Artificial Intelligence for AIG (aka Natural Question Generation-NQG), states that even newer AIG techniques are short in syntactic, semantic, and contextual relevance when evaluated qualitatively on small datasets. We confirm this deficiency quantitatively over large datasets. Additionally, we find that human evaluation by Subject Matter Experts (SMEs) conservatively rejects at least g1/49% portion of AI test questions in our experiment over large diverse dataset topics. Here we present an analytical study of these differences, and this motivates our two-phased post-processing AI daisy chain machine learning (ML) architecture for selection and editing of AI generated questions using current techniques. Finally, we identify and propose the first selection step in the daisy chain using ML with 97+% accuracy, and provide analytical guidance for development of the second editing step with a measured lower bound on a BLEU score improvement of 2.4+%. © 2023 ACM.

关键词： Semantics

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An Energy-Efficient Human Activity Recognition System Based on Smartphones 7

An Energy-Efficient Human Activity Recognition System Based ...

引用

7th international conference on soft computing and machine Intelligence (ISCMI)

作者： Shi, Junhao Zuo, Decheng Zhang, Zhan Harbin Inst Technol Dept Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9781728175591

Smartphone-based human activity recognition (HAR) has become a considerable research field as a subdomains of pattern recognition and pervasive computing. With the increasing popularity of smartphones, LIAR has prominent applications in number of fields such as health care, education, entertainment and etc. Smart devices have a huge advantage in convenience as the main acquisition and processing equipment, but the battery life of smartphone and other resources are limited for long-duration tasks. In this paper, we propose a lightweight HAR system. the system realizes HAR algorithm with deep learning algorithm. Beyond that, we introduce a clustering-center based pre-classification strategy to reduce the call frequency of the DL model. Meanwhile, we add a sampling frequency control mechanism to the inertial sensor. the goal of the whole system is to achieve low power consumption and time delay. According to the final experiment results, the energy consumption reduces about 49% and time delay reduces about 55% while the overall recognition accuracy only suffers about 10% reduction.

关键词： human activity recognition energy-efficient deep learning pervasive computing smartphone application

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Deep Neural Networks for Lie Detection with Attention on Bio-signals 7

Deep Neural Networks for Lie Detection with Attention on Bio...

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7th international conference on soft computing and machine Intelligence (ISCMI)

作者： Bhamare, Ameya Rajendra Katharguppe, Srinivas Nancy, Silviya J. PES Univ Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781728175591

After years of research, scientists have consistently proven that the ability of people to detect deception is no greater than chance. Accurate deception detection is crucial for law enforcement officials. the conventional polygraph test is far from perfect. It can be deceived by a trained individual quite easily. Micro-expressions are difficult to control or fake. they occur as fast as 1/15th of a second. Speech is another feature that can be used to catch a liar. they often speak in high or low-pitched voices due to tightening of their vocal cords, or pause to think before answering. In this paper, we have come up with a robust solution. It is a deep learning model that combines micro-expressions along with audio features and is used to detect lies. this proposed model is essentially a lie detector and has vast applications. For instance, it can assist law enforcement officials during interrogations and human resource personnel while conducting corporate interviews.

关键词： deception detection deep learning micro expressions audio features facial landmarks

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A Comprehensive Diagnostic Tool for Skin Cancer Using a Multifaceted Computer Vision Approach 7

A Comprehensive Diagnostic Tool for Skin Cancer Using a Mult...

引用

7th international conference on soft computing and machine Intelligence (ISCMI)

作者： Srinivasan, Pranav Srinivasan, Varun Westwood High Sch Austin TX 78750 USA

ISBN: (纸本)9781728175591

this paper introduces a system that has the capability to effectively diagnose a skin lesion through machine learning computer vision techniques that enable real-time assessment. this enables individuals to efficiently diagnose themselves and get the necessary treatment quickly. In addition, this system is also available for clinics. permitting easy and inexpensive diagnosis, as opposed to traditional diagnosis methods such as Magnetic Resonance Imaging devices (MRI), or Computed Tomography scans (CT). We design an approach that combines Object Detection with an ensemble of Convolutional Neural Networks (CNN) and encapsulates it in a button based user-navigable Graphical User Interface (GUI), leveraging the video input from a user's webcam (either built-in or external) to locate and identify the lesion instantaneously. Additionally, the tool provides critical information about the diagnosis, as well as links to medical resources and treatment options. We achieve a top-1 accuracy of 87% +/- 2.0%, pointing to our tool as a viable and highly accurate self-diagnosis strategy for individuals globally. Our system can potentially reach those in areas where access to diagnostic tools and healthcare professionals is seriously lacking.

关键词： machine learning computer vision medical diagnosis skin lesion

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Federated learning based Object Detection using Dampened Harmonic Optimization 23

Federated Learning based Object Detection using Dampened Har...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Jain, Deepak Kumar Khanna, Akshit Gera, Bhavya Sangwan, Dhiraj Chongqing University of Posts and Telecommunications Chongqing China Birla Institute of Technology and Science Rajasthan Pilani India CSIR-CEERI Rajasthan Pilani India

ISBN: (纸本)9781450398633

Object Detection is the task of detecting and localizing objects of importance in visual media. the rapid increase in the number of powerful end devices such as mobiles and surveillance cameras have to lead to increasing in both resources and media generation. Object Detection has now become possible on end devices, but certain challenges need to be tackled to fully utilize the resources. Federated learning is one such framework that leverages the end device resources to build machine learning models while preserving data privacy. We model the federated learning framework for object detection on real-world heterogeneous datasets using a novel dampened harmonic optimizer to enhance local learning on the end device and hence reducing the communication cost during the learning process. We provide comparison of the commonly used FedAvg with our FedHarm optimization on multiple object detection models and datasets to demonstrate the merits of our proposition. Our method FedHarm with its dampened updates allows for greater local computation which reduces the overall communication rounds between edge devices and cloud and better handles heterogeneity in real-world datasets. FedHarm leads to faster convergence by 41% on average over FedAvg which is supported by extensive experiments. © 2023 ACM.

关键词： Object detection

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