Zobrazeno 1 - 10
of 3 567
pro vyhledávání: '"SAJID, M."'
In the domain of machine learning, least square twin support vector machine (LSTSVM) stands out as one of the state-of-the-art models. However, LSTSVM suffers from sensitivity to noise and outliers, overlooking the SRM principle and instability in re
Externí odkaz:
http://arxiv.org/abs/2410.17338
Publikováno v:
27th International Conference on Pattern Recognition (ICPR), 2024
In this paper, we propose enhanced feature based granular ball twin support vector machine (EF-GBTSVM). EF-GBTSVM employs the coarse granularity of granular balls (GBs) as input rather than individual data samples. The GBs are mapped to the feature s
Externí odkaz:
http://arxiv.org/abs/2410.05786
Publikováno v:
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture ModelsTwin support vector machine (TSVM) is an emerging machine learning model with versatile applicability in classification and regression endeavors.
Externí odkaz:
http://arxiv.org/abs/2410.04774
The random vector functional link (RVFL) network is a prominent classification model with strong generalization ability. However, RVFL treats all samples uniformly, ignoring whether they are pure or noisy, and its scalability is limited due to the ne
Externí odkaz:
http://arxiv.org/abs/2409.16735
The classification performance of the random vector functional link (RVFL), a randomized neural network, has been widely acknowledged. However, due to its shallow learning nature, RVFL often fails to consider all the relevant information available in
Externí odkaz:
http://arxiv.org/abs/2409.04743
The identification of DNA-binding proteins (DBPs) is a critical task due to their significant impact on various biological activities. Understanding the mechanisms underlying protein-DNA interactions is essential for elucidating various life activiti
Externí odkaz:
http://arxiv.org/abs/2409.02588
Autor:
Jain, Arnav, Sanjotra, Jasmer Singh, Choudhary, Harshvardhan, Agrawal, Krish, Shah, Rupal, Jha, Rohan, Sajid, M., Hussain, Amir, Tanveer, M.
Publikováno v:
INTERSPEECH 2024
In this paper, we propose long short term memory speech enhancement network (LSTMSE-Net), an audio-visual speech enhancement (AVSE) method. This innovative method leverages the complementary nature of visual and audio information to boost the quality
Externí odkaz:
http://arxiv.org/abs/2409.02266
Publikováno v:
31st International Conference on Neural Information Processing (ICONIP2024), New Zealand
The random vector functional link (RVFL) network is well-regarded for its strong generalization capabilities in the field of machine learning. However, its inherent dependencies on the square loss function make it susceptible to noise and outliers. F
Externí odkaz:
http://arxiv.org/abs/2408.02824
Publikováno v:
IEEE Transactions on Fuzzy Systems, 2024
The ensemble deep random vector functional link (edRVFL) neural network has demonstrated the ability to address the limitations of conventional artificial neural networks. However, since edRVFL generates features for its hidden layers through random
Externí odkaz:
http://arxiv.org/abs/2406.00801