Bilaketaren emaitzak - "Machine Learning"

Corrigendum: Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study nork Ratul Bhowmik, Ravi Kant, Ajay Manaithiya, Daman Saluja, Bharti Vyas, Ranajit Nath, Kamal A. Qureshi, Seppo Parkkila, Seppo Parkkila, Ashok Aspatwar

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Predicting missed health care visits during the COVID-19 pandemic using machine learning methods: evidence from 55,500 individuals from 28 European countries nork Anna Reuter, Šime Smolić, Till Bärnighausen, Nikkil Sudharsanan

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Label‐free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface‐enhanced Raman spectroscopy validated by machine learning algorithm nork Sanghwa Lee, Miyeon Jue, Minju Cho, Kwanhee Lee, Bjorn Paulson, Hanjoong Jo, Joon Seon Song, Soo‐Jin Kang, Jun Ki Kim

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Correction: Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm nork Michiko Ueda, Kohei Watanabe, Hajime Sueki

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Towards development of robust machine learning model for Malaysian corporation: a systematic review of essential aspects for corporate credit risk assessment / Zulkifli Halim and Shuhaida Mohamed Shuhidan nork Halim, Zulkifli, Mohamed Shuhidan, Shuhaida

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Web scraping and regression analysis based on machine learning for COVID-19 with rapid software platform / Aizal Yusrina Idris, Razan Bamoallem and Mohamad Harith Azfar Mohamad Hatta nork Idris, Aizal Yusrina, Bamoallem, Razan, Mohamad Hatta, Mohamad Harith Azfar

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On the path to AI Law's prophecies and the conceptual foundations of the machine learning age / nork Grant, Thomas D., Wischik, Damon J.

Aurkibidea: “...Prologue: Starting with logic -- CHAPTER 1: Two Revolutions -- CHAPTER 2: Getting past logic -- CHAPTER 3: Experience and data as input -- CHAPTER 4: Finding patterns as the path from input to output -- CHAPTER 5: Output as prophecy -- CHAPTER 6: Explanations of machine learning -- CHAPTER 7: Juries and other reliable predictors -- CHAPTER 8: Poisonous datasets, poisonous trees -- CHAPTER 9: From Holmes to AlphaGo -- CHAPTER 10:Conclusion -- EPILOGUE: Lessons in two directions....”
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Automation and Control Trends

Gaiak: “...Machine learning bicssc...”
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The Science of Emotional Intelligence

Gaiak: “...machine learning, eeg, emotions, education, leadership, stress...”
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Updates in Volcanology Transdisciplinary Nature of Volcano Science

Gaiak: “...machine learning, geochemistry, fractionation, karst, cameroon...”
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Applied Artificial Neural Networks nork Christian Dawson (Ed.)

Gaiak: “...Machine Learning...”
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Efficient Reinforcement Learning using Gaussian Processes nork Deisenroth, Marc Peter

Gaiak: “...machine learning...”
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Advances in Intelligent Data Analysis XVIII 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings /

Aurkibidea: “...Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder -- Dual Sequential Variational Autoencoders for Fraud Detection -- A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks -- Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams -- GraphMDL: Graph Pattern Selection Based on Minimum Description Length -- Towards Content Sensitivity Analysis -- Gibbs Sampling Subjectively Interesting Tiles -- Even Faster Exact k-Means Clustering -- Ising-Based Consensus Clustering on Special Purpose Hardware -- Transfer Learning by Learning Projections from Target to Source -- Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs -- Towards Evaluation of CNN Performance in Semantically Meaningful Latent Spaces -- Vouw: Geometric Pattern Mining Using the MDL Principle -- A Consensus Approach to Improve NMF Document Clustering -- Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams -- Widening for MDL-Based Retail Signature Discovery -- Addressing the Resolution Limit and the Field of View Limit in Community Mining -- Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics -- Adversarial Attacks Hidden in Plain Sight -- Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code -- Overlapping Hierarchical Clustering (OHC) -- Digital Footprints of International Migration on Twitter -- Percolation-Based Detection of Anomalous Subgraphs in Complex Networks -- A Late-Fusion Approach to Community Detection in Attributed Networks -- Reconciling Predictions in the Regression Setting: an Application to Bus Travel Time Prediction -- A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization -- Actionable Subgroup Discovery and Urban Farm Optimization -- AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model -- Detection ofDerivative Discontinuities in Observational Data -- Improving Prediction with Causal Probabilistic Variables -- DO-U-Net for Segmentation and Counting -- Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media -- Event Recognition Based on Classification of Generated Image Captions -- Human-to-AI Coach: Improving Human Inputs to AI Systems -- Aleatoric and Epistemic Uncertainty with Random Forests -- Master your Metrics with Calibration -- Supervised Phrase-Boundary Embeddings -- Predicting Remaining Useful Life with Similarity-Based Priors -- Orometric Methods in Bounded Metric Data -- Interpretable Neuron Structuring with Graph Spectral Regularization -- Comparing the Preservation of Network Properties by Graph Embeddings -- Making Learners (More) Monotone -- Combining Machine Learning and Simulation to a Hybrid Modelling Approach -- LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification -- Angle-Based Crowding Degree Estimation for Many-Objective Optimization....”
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Multimedia Forensics

Gaiak: “...Machine learning....”
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Hyperparameter Tuning for Machine and Deep Learning with R A Practical Guide

Gaiak: “...Machine learning bicssc...”
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Learning to Quantify nork Esuli, Andrea

Gaiak: “...Machine learning bicssc...”
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Hyperparameter Tuning for Machine and Deep Learning with R A Practical Guide

Gaiak: “...Machine learning bicssc...”
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Learning to Quantify nork Esuli, Andrea

Gaiak: “...Machine learning bicssc...”
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Process Mining Workshops ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23-28, 2022, Revised Selected Papers /

Gaiak: “...Machine learning....”
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A Multi-Machine Learning Consensus Model Based on Clinical Features Reveals That Interleukin-10 Derived from Monocytes Leads to a Poor Prognosis in Patients with Coronavirus Disease-2019 nork Yu J, Zhang Y, Ye Z, Tang K, Ma Y, Fu L, Cui T, Kang H, Yuan Y, Pan W

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