Results 21 to 30 of about 98,022 (269)
Automatic Hyperparameter Tuning in Sparse Matrix Factorization
Neural Computation, 2023 Abstract We study the problem of hyperparameter tuning in sparse matrix factorization under a Bayesian framework. In prior work, an analytical solution of sparse matrix factorization with Laplace prior was obtained by a variational Bayes method under several approximations.
Kawasumi, Ryota, Takeda, Koujin
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Convolutional Neural Networks for Sentence Classification [PDF]
, 2014 We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.
Kim, Yoon
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Hyperparameter tuning in echo state networks
Proceedings of the Genetic and Evolutionary Computation Conference, 2022 Echo State Networks represent a type of recurrent neural network with a large randomly generated reservoir and a small number of readout connections trained via linear regression. The most common topology of the reservoir is a fully connected network of up to thousands of neurons.
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EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE [PDF]
Proceedings on Engineering SciencesThis paper presents a novel approach for hyperparameter optimization for the MobileNetV2 architecture using a genetic algorithm. The proposed approach aims to automate the hyperparameter tuning leading to performance enhancement.
Baljinder Kaur +3 more
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Elastic Hyperparameter Tuning on the Cloud [PDF]
Proceedings of the ACM Symposium on Cloud Computing, 2021 Hyperparameter tuning is a necessary step in training and deploying machine learning models. Most prior work on hyperparameter tuning has studied methods for maximizing model accuracy under a time constraint, assuming a fixed cluster size. While this is appropriate in data center environments, the increased deployment of machine learning workloads in ...
Lisa Dunlap +6 more
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Hyperparameter self-tuning for data streams [PDF]
Information Fusion, 2021 Abstract The number of Internet of Things devices generating data streams is expected to grow exponentially with the support of emergent technologies such as 5G networks. Therefore, the online processing of these data streams requires the design and development of suitable machine learning algorithms, able to learn online, as data is generated.
Veloso, Bruno +3 more
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Hyperparameters and tuning strategies for random forest [PDF]
WIREs Data Mining and Knowledge Discovery, 2019 The random forest (RF) algorithm has several hyperparameters that have to be set by the user, for example, the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples that a node must contain, and ...
Probst, Philipp +2 more
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Effect of Hyperparameter Tuning on Performance on Classification model
International Journal of Applied Sciences and Smart TechnologiesThis research aims to analyze the effect of hyperparameter tuning on the performance of Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest, Random Forest Classifier, Naive Bayes algorithms.
Muhammad Sholeh +2 more
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Parameter Tuning Using Harris Hawks Optimization for Improved Chronic Kidney Disease Classification
مجلة التربية والعلمAt an early phase, chronic kidney disease (CKD) is usually not obvious. An appreciable reduction in kidney function is the primary sign of the disease.
Omar Shakir Hasan +1 more
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Sherpa: Robust hyperparameter optimization for machine learning
SoftwareX, 2020 Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations, such as the hyperparameter tuning of deep neural networks.
Lars Hertel +4 more
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