Search Results - multiple-(instance OR instalacie) learning algorithms*

Visualización de conjuntos de datos de múltiples instancias by Valencia-Duque, Jorge Eliecer, Mera, Carlos, Sepúlveda, Lina Maria

“…In pattern recognition, multiple-instance learning algorithms have gained importance since they avoid that the user must delimit, the images individually in order to recognize the objects…”
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Visualizacion de conjuntos de datos de multiples instancias/Visualization multi-instance data sets by Valencia-Duque, Jorge Eliecer, Mera, Carlos, Sepúlveda, Lina Maria

“…En el reconocimiento de patrones, los algoritmos de aprendizaje de múltiples instancias han ganado importancia puesto que evitan que el usuario tenga que delimitar…”
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Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation by Zhu, Wenhui, Qiu, Peijie, Lepore, Natasha, Dumitrascu, Oana M, Wang, Yalin

“… This paper leverages self-supervised equivariant learning and attention-based multi-instance learning (MIL…”
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Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring From Ballistocardiograms by Jiao, Changzhe, Su, Bo-Yu, Lyons, Princess, Zare, Alina, Ho, K. C., Skubic, Marjorie

“…A multiple instance dictionary learning approach, dictionary learning using functions of multiple instances (DL-FUMI…”
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One-Class Classification Using ℓp-Norm Multiple Kernel Fisher Null Approach by Arashloo, Shervin Rahimzadeh

“… (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an ℓ…”
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mi-DS: Multiple-Instance Learning Algorithm by Nguyen, Dat T., Nguyen, Cao D., Hargraves, Rosalyn, Kurgan, Lukasz A., Cios, Krzysztof J.

“…Multiple-instance learning (MIL) is a supervised learning technique that addresses the problem of classifying bags of instances instead of single instances…”
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Predicting Nottingham grade in breast cancer digital pathology using a foundation model by Kim, Jun Seo, Lee, Jeong Hoon, Yeon, Yousung, An, Doyeon, Kim, Seok Jun, Noh, Myung-Giun, Lee, Suehyun

“…Background The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide…”
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Data Subset Selection With Imperfect Multiple Labels by Fang, Meng, Zhou, Tianyi, Yin, Jie, Wang, Yang, Tao, Dacheng

“…We study the problem of selecting a subset of weakly labeled data where the labels of each data instance are redundant and imperfect…”
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A Novel Multiple Instance Learning Method Based on Extreme Learning Machine by Jia, Yuheng, Peng, Jinzhu, Cai, Liangjian, Wang, Jie

“…Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm…”
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Development of a deep learning system for predicting biochemical recurrence in prostate cancer by Cao, Lu, He, Ruimin, Zhang, Ao, Li, Lingmei, Cao, Wenfeng, Liu, Ning, Zhang, Peisen

“… The multiple instance learning method was used…”
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Multilabel Ranking With Inconsistent Rankers by Geng, Xin, Zheng, Renyi, Lv, Jiaqi, Zhang, Yu

“… where only subjective inconsistent rankings from multiple rankers are associated with each instance. Two ranking methods are proposed from the perspective of instances and rankers, respectively…”
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A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies by Khondoker, Mizanur, Dobson, Richard, Skirrow, Caroline, Simmons, Andrew, Stahl, Daniel

“…Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies…”
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Scheduling of Continuous Annealing With a Multi-Objective Differential Evolution Algorithm Based on Deep Reinforcement Learning by Li, Tianyang, Meng, Ying, Tang, Lixin

“… In this paper, we formulate a multi-objective optimization model for the problem and develop an adaptive multi-objective differential evolutionary based on deep reinforcement learning (AMODE-DRL…”
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Robust visual tracking via randomly projected instance learning by Cheng, Fei, Liu, Kai, Gong, Mao-Guo, Fu, Kaiyuan, Xi, Jiangbo

“… First, the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling…”
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CGDD: Multiview Graph Clustering via Cross-Graph Diversity Detection by Huang, Shudong, Tsang, Ivor W., Xu, Zenglin, Lv, Jiancheng

“…Multiview graph clustering has emerged as an important yet challenging technique due to the difficulty of exploiting the similarity relationships among multiple views…”
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Attention-Based Deep Learning System for Classification of Breast Lesions—Multimodal, Weakly Supervised Approach by Bobowicz, Maciej, Rygusik, Marlena, Buler, Jakub, Buler, Rafał, Ferlin, Maria, Kwasigroch, Arkadiusz, Szurowska, Edyta, Grochowski, Michał

“… We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM…”
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A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients by Ru, Xiaoshuang, Zhao, Shilong, Chen, Weidao, Wu, Jiangfen, Yu, Ruize, Wang, Dawei, Dong, Mengxing, Wu, Qiong, Peng, Daoyong, Song, Yang

“… We aimed to develop a deep learning method for predicting HT after IVT for AIS using noncontrast computed tomography (NCCT) images…”
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Federated Multi-View K-Means Clustering by Yang, Miin-Shen, Sinaga, Kristina P.

“… This work integrates the exponential distance to transform the weighted Euclidean distance on MVKM so that it can make full use of development in federated learning via the MVKM clustering algorithm…”
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Weakly Supervised Temporal Action Localization Through Contrast Based Evaluation Networks by Liu, Ziyi, Wang, Le, Zhang, Qilin, Tang, Wei, Zheng, Nanning, Hua, Gang

“… However, the coarse video-level supervision inevitably incurs ambiguities in action localization, especially in untrimmed videos containing multiple action instances…”
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Efficient Source Camera Identification with Diversity-Enhanced Patch Selection and Deep Residual Prediction by Liu, Yunxia, Zou, Zeyu, Yang, Yang, Law, Ngai-Fong Bonnie, Bharath, Anil Anthony

“… Besides conventional feature engineering algorithms developed based on studying the traces left upon shooting, several deep-learning-based methods have also emerged recently…”
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