Prototype-based classification

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August undefined, 2024

Webb1 sep. 2013 · In this paper, a novel projected-prototype based classifier is proposed for text categorization, in which a document category is represented by a set of prototypes, each assembling a representative for the documents in a subclass and its corresponding term subspace. Webb16 maj 2024 · In our PCL, we propose to generate the categorical classifiers based on the prototypes by performing a learnable mapping function. To further alleviate the impact …

机器学习领域prototype learning是什么？ - 知乎

Webb30 maj 2024 · An introduction is given to the use of prototype-based models in supervised machine learning. The main concept of the framework is to represent previously … WebbThereafter, a Classifier Fusion Strategies (CFS) is invoked as a post-processing module, so as to combine the individual KNS classification results to derive a consensus decision. Our experimental results demonstrate that the proposed mechanism significantly reduces the prototype extraction time as well as the computation time without sacrificing the … st pius parish chicago

Prototype theory - Wikipedia

Webb28 feb. 2024 · Deep neural network (DNN) based on incremental learning provides support for efficient garbage classification tasks. However, it is always challenging to accurately learn and preserve the information of known classes for updating DNN while new tasks are continuously emerging, which also affects the generalization performance of the model. … Webb8 jan. 2016 · A particularly unique advantage of prototype-based methods is the narrow barrier in transitioning the learned classifier to a production system. In this paper, we … Webb3 maj 2024 · Causality-based Counterfactual Explanation for Classification Models Tri Dung Duong, Qian Li, Guandong Xu Counterfactual explanation is one branch of … st pius parish little suamico wi

Quantum-inspired learning vector quantizers for prototype-based ...

Decomposed Meta-Learning for Few-Shot Sequence Labeling

WebbThe prototype of a category can be understood in lay terms by the object or member of a class most often associated with that class. The prototype is the center of the class, … Webb3 dec. 2024 · Prototype-based methods use interpretable representations to address the black-box nature of deep learning models, in contrast to post-hoc explanation methods that only approximate such models. We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image recognition. … st pius parish providenceWebb8 jan. 2016 · A particularly unique advantage of prototype-based methods is the narrow barrier in transitioning the learned classifier to a production system. In this paper, we address a specific practical application; the discrimination of four diseases in cassava plants by analysing images of their leaves. st pius parish appleton

"Webb1 dec. 2024 · Prototypes play an instrumental role in prototype-based classification algorithms. Different prototype-based approaches use different ways to identify prototypes from data, and this creates the differences in performance, computational efficiency, system transparency and interpretability. " - Prototype-based classification

Prototype-based classification

Prototype Enhancement-Based Incremental Evolution Learning for …

Webb1 okt. 2024 · This paper introduces a novel self-training hierarchical prototype-based approach for semi-supervised classification.The proposed approach firstly identifies meaningful prototypes from labelled samples at multiple levels of granularity and, then, self-organizes a highly transparent, multi-layered recognition model by arranging them in … Webb16 sep. 2024 · Our approach has been designed to enable the integration of prototype-based interpretable model to any highly accurate global mammogram classifier, where …

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Webb11 juni 2013 · Fuzzy prototype-based classifiers can be related to popular classifier models including Parzen classifier, learning vector quantization (LVQ) and radial basis functions (RBF) neural networks (Kuncheva 2000). Along with training from data, human expertise can be used. Webb3 maj 2024 · A prototype-based counterfactual explanation framework (ProCE) is proposed that is capable of preserving the causal relationship underlying the features …

Webb24 feb. 2024 · This paper presents a novel low-cost integrated system prototype, called School Violence Detection system (SVD), based on a 2D Convolutional Neural Network (CNN). It is used for classifying and identifying automatically violent actions in educational environments based on shallow cost hardware. Moreover, the paper fills the gap of real … WebbFör 1 dag sedan · Due to the limitations of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) appears as a …

Webb22 jan. 2016 · Prototype-based models, in particular, which are mainly described by vector quantization methods [8]- [10], have a number of appealing properties for clustering and classification [11]. They... WebbClass Prototypes based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos Rohit Gupta · Anirban Roy · Sujeong Kim · Claire Christensen · Todd Grindal · Sarah Gerard · Madeline Cincebeaux · Ajay Divakaran · Mubarak Shah MaskCon: …

WebbFör 1 dag sedan · Due to the limitations of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) appears as a vibrant prospect in WSI classification. However, the pseudo-bag dividing scheme, often crucial for classification performance, is still an open topic worth exploring. Therefore, this paper …

Webb1 juni 2008 · We call the system catalogue-based image classifier. The system is provided with feature-subset selection, feature weighting, and prototype selection. The performance of the catalogue-based classifier is assessed by studying the accuracy and the reduction of the prototypes after applying a prototype-selection algorithm. st pius parish lynn maWebb28 dec. 2024 · The optimization-based methods [4,5] use an alternate optimization strategy to learn how to update model parameters more quickly. As a result, the networks have a good initialization, updated direction, and learning rate to adapt quickly to tasks. The metric-based methods classify samples by distinguishing different distances between … st pius primary school chelmsfordWebb24 nov. 2024 · Prototype-based models like the Generalized Learning Vector Quantization (GLVQ) belong to the class of interpretable classifiers. Moreover, quantum-inspired methods get more and more into focus in machine learning due to its potential efficient computing. Further, its interesting mathematical perspectives offer new ideas for … st pius rc primary school bristol st pius portland meWebb1 juni 2008 · A prototype-based classification system for medical image interpretation is described in [48]. It realizes all the functions described above by combining statistical … roth ira investment deductionWebb2 dec. 2015 · Section snippets Prototype-based classification. We are interested in classification scenarios in R n with Z classes, enumerated as {1, …, Z}.Prototype-based classifiers are defined as follows: a set W of prototypes (w j, c (w j)) ∈ R n × {1, …, Z}, j ∈ {1, …, w} is specified which should represent the data and its underlying classes in a proper … st pius preschoolWebb16 jan. 2024 · Processing big data streams through machine learning algorithms has various challenges, such as little time to train the models, hardware memory constraints, and concept drift. In this paper, we show that prototype-based kernel classifiers designed by sparsification procedures, such as the approximate linear dependence (ALD) method, … roth ira investment growth calculator