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检索条件"任意字段=19th International Conference on Algorithmic Learning Theory"

共 547 条记录，以下是1-10 订阅

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algorithmic learning theory - 19th international conference, ALT 2008, Proceedings

Algorithmic Learning Theory - 19th International Conference,...

引用

19th international conference on algorithmic learning theory, ALT 2008

the proceedings contain 32 papers. the topics discussed include: on iterative algorithms with an information geometry background;some mathematics behind graph property testing;finding total and partial orders from data for seriation;generalization bounds for some ordinal regression algorithms;approximation of the optimal ROC curve and a tree-based ranking algorithm;sample selection bias correction theory;exploiting cluster-structure to predict the labeling of a graph;a uniform lower bounds for K-dimensional coding schemes in Hilbert spaces;learning and generalization with the information bottleneck;growth optimal investment with transaction costs;online regret bounds for Markov decision processes with deterministic transitions;on-line probability, complexity and randomness;and sufficient conditions on an arbitrary class of stochastic processes for the existence of a predictor.

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algorithmic learning theory: 14th international conference, ALT 2003, Sapporo, Japan, October 17-19, 2003. Proceedings 1

引用

丛书名： Lecture notes in computer science,

2003年

作者： thomas Eiter (auth.) Ricard Gavaldá Klaus P. Jantke Eiji Takimoto (eds.)

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Comparative Analysis of Machine learning Algorithms and Ensemble Techniques for Diverse Image Classification Tasks 19

Comparative Analysis of Machine Learning Algorithms and Ense...

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Salim, Yulita Rakasyah, Athar Fathana Darwis, Herdianti Harlinda Irawati Manga, Abdul Rachman Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

In the era of burgeoning image data, the demand for robust image classification algorithms has never been more pressing. this research article delves into the realm of image classification, encompassing a comprehensive analysis of diverse datasets and machine learning algorithms, While simultaneously studying the efficiency of ensemble approaches in improving classification performance. the study employs five distinct datasets, spanning medical images, everyday objects, and natural scenery, ensuring a broad spectrum of classification challenges. these datasets include the Skin Cancer ISIC dataset, CIFAR-10, Flowers, Apparel Image, and Brain Tumor dataset. the image classes within these datasets vary significantly, presenting an ideal testbed for assessing algorithmic versatility. Our investigation scrutinizes five machine learning algorithms: Support Vector Classifier (SVC), Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), and Gaussian Naive Bayes (GNB). Each algorithm is evaluated individually, followed by a comprehensive ensemble approach involving a Voting Classifier. the results unveil nuanced performance variations across diverse datasets. Notably, RFC and GBC exhibit remarkable accuracy in brain tumor image classification, while KNN demonstrates strengths in classifying apparel images. Ensemble techniques, embodied by the Voting Classifier, harmonize these algorithms, yielding competitive and balanced performance across the datasets. this article contributes valuable insights into the realm of image classification, shedding light on algorithmic strengths and limitations, the efficacy of ensemble techniques, and their applicability to diverse image datasets. these findings hold significance for fields ranging from medical diagnostics to everyday object recognition, paving the way for more precise and versatile image classification solution. © 2025 IEEE.

关键词： Adversarial machine learning

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引用

19th international conference on Tangible Embedded and Embodied Interaction

作者： Liscio, Christopher Brown, Daniel G. Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9798400711978

Popular musicians often learn songs by ear, from recordings, using technology that provides control over playback events. However, we know little about these human-recording interactions, and our research goal is to develop novel and useful ones. Guided by a preliminary study of 18 YouTube videos of musicians learning by ear, we performed a grounded-theory analysis of 28 YouTube lessons and an instructional DVD that teaches by-ear learning. Based on our findings, we characterize the sub-tasks musicians follow to learn songs from recordings, their variations, and discuss how memory plays a role in ear learning. Armed with these insights, we offer recommendations for the design of four novel, memory-oriented human-recording interactions-facilitating active listening sessions, restricting playback to fit working memory, extracting musical sequences for memorization, and re-synthesizing the recording to play notes indefinitely-all of which are grounded in real-world observations, and studies in both neuroscience and psychology.

关键词： learning music by ear popular music education YouTube content analysis digital ethnography

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Mediated Effect of Product Quality on Product Familiarity, Low-Price, and Social Interactions Affecting Urge to Buy Impulsively in Live-Streaming Shopping 19

Mediated Effect of Product Quality on Product Familiarity, L...

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Ko, Hsiu-Chia Department of Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9798331507817

Live-streaming shopping (LSS) quickly monetizes traffic by triggering the urge to buy impulsively (UBI). For experienced LSS consumers, past viewing memories and marketing stimuli may influence their UBI. Grounded in the stimuli-organism-response theory and social interaction theory, this study aims to explore how product familiarity, monetary savings, social interactions, and product quality influence UBI. the mediating role of product quality between these stimuli and UBI is also examined. Data were collected from an online questionnaire of 233 experienced LSS participants. the results reveal that product familiarity, perceived monetary savings, and word of mouth enhance perceived product quality, which in turn positively affects UBI. While observing others’ purchases directly drives UBI, text-based positive word of mouth influences UBI through perceived product quality. this study shows that for experienced LSS consumers, UBI can be driven by cognitive factors, with perceived product quality as a crucial mediator. the findings provide valuable insights for streamers to enhance consumer engagement and drive sales through effective marketing stimuli and strong social interactions. © 2025 IEEE.

关键词： live-streaming shopping monetary savings observational learning product familiarity product quality urge to buy impulsively word of mouth

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Artificial Intelligence in Portfolio Selection Problem: A Review and Future Perspectives 19th

Artificial Intelligence in Portfolio Selection Problem: A Re...

引用

19th international conference on Hybrid Artificial Intelligence Systems

作者： Sanchez-Fernandez, Alvaro Diez-Gonzalez, Javier Perez, Hilde Univ Leon Dept Mech Comp & Aerosp Engn Leon 24071 Spain

ISBN: (纸本)9783031741852;9783031741869

Portfolio selection, the process of selecting the optimal combination of securities to achieve investors objectives, is a leading problem in finance. Ever since its inception, the problem has been widely addressed theoretically but its practical applications in portfolio management have seen few changes, with new technologies like Artificial Intelligence (AI) having limited impact in practice. the present study addresses this gap by identifying six main areas of the portfolio selection process and analyzing literature trends and gaps in AI methods within these areas. Additionally, it proposes four primary research avenues where AI can enhance portfolio selection in both theory and practice: i) utilizing alternative datasets and big-data analysis, ii) implementing robust predictive portfolio optimization techniques, iii) applying Reinforcement learning (RL) methods for dynamic portfolio optimization, and iv) leveraging AI to make portfolio optimization more applicable in real-world scenarios.

关键词： Artificial Intelligence Machine learning Metaheuristics Portfolio Optimization Portfolio Selection

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

引用

19th international conference on Computer Aided Systems theory, EUROCAST 2024

ISBN: (纸本)9783031829598

the proceedings contain 104 papers. the special focus in this conference is on Computer Aided Systems theory. the topics include: Influence of Spike Encoding, Neuron Models and Quantization on SNN Performance;adaptive Combination in Frequency Domain: An Approach for Robust Nonlinear Acoustic Echo Cancellation;using of a Robotic Platform to Detect Acoustic Events for Indoor Environments;modeling Wildlife Accident Risk with Gaussian Mixture Models;towards a Unified Incident Detection and Response System for Autonomous Transportation;edge-Processing of Myoelectric Signals for the Control of Hand and Arm-Prostheses;AI-Driven Gesture and Action Recognition for learning Medicine through Virtual Reality;Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices;a Survey of Machine learning Methods for Analyzing Synovitis Arthritis in Human Joints;motion Tracking in Augmented and Mixed Realities for Healthcare and Medicine Applications;advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, therapy, and Rehabilitation;Transformation of IEC 61131-3 onto an Embedded Platform Using LLVM;machine learning Based Parameter Estimation of Energy Models in Digital Production Environments;efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine learning Methods;machine learning Using a Hybrid Quantum Classical Algorithm with Amplitude Data Encoding;quantitative Trend Analysis of Reinforcement learning Algorithms in Production Systems;Using AutomationML for Advanced Simulation in Industrial Automation;AR Digital Twin Demonstrator for Industrial Robotics Education;Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition;variety Engineering – A Cybernetic Concept with Practical Implications;using a System Archetype to Explore a Business Model for Digital Textile Microfactories;interacting with the Water Cycle -

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

ISBN: (纸本)9783031838873

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

19th International Conference on Computer Aided Systems Theo...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

ISBN: (纸本)9783031829512

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Comparative Analysis of Machine learning Algorithms and Ensemble Techniques for Diverse Image Classification Tasks

Comparative Analysis of Machine Learning Algorithms and Ense...

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international conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yulita Salim Athar Fathana Rakasyah Herdianti Darwis Harlinda Irawati Abdul Rachman Manga Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

In the era of burgeoning image data, the demand for robust image classification algorithms has never been more pressing. this research article delves into the realm of image classification, encompassing a comprehensive analysis of diverse datasets and machine learning algorithms, While simultaneously studying the efficiency of ensemble approaches in improving classification performance. the study employs five distinct datasets, spanning medical images, everyday objects, and natural scenery, ensuring a broad spectrum of classification challenges. these datasets include the Skin Cancer ISIC dataset, CIFAR-10, Flowers, Apparel Image, and Brain Tumor dataset. the image classes within these data sets vary significantly, presenting an ideal testbed for assessing algorithmic versatility. Our investigation scrutinizes five machine learning algorithms: Support Vector Classifier (SVC), Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), and Gaussian Naive Bayes (GNB). Each algorithm is evaluated individually, followed by a comprehensive ensemble approach involving a Voting Classifier. the results unveil nuanced performance variations across diverse datasets. Notably, RFC and GBC exhibit remarkable accuracy in brain tumor image classification, while KNN demonstrates strengths in classifying apparel images. Ensemble techniques, embodied by the Voting Classifier, harmonize these algorithms, yielding competitive and balanced performance across the datasets. this article contributes valuable insights into the realm of image classification, shedding light on algorithmic strengths and limitations, the efficacy of ensemble techniques, and their applicability to diverse image datasets. these findings hold significance for fields ranging from medical diagnostics to everyday object recognition, paving the way for more precise and versatile image classification solution

关键词： Machine learning algorithms Accuracy Heuristic algorithms Brain tumors Nearest neighbor methods Classification algorithms Complexity theory Ensemble learning Random forests Image classification

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