Cryptocurrency Q&A
Why is SVM not popular nowadays?
Why is SVM not popular nowadays?
Tommaso
Wed Sep 11 2024
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6 answers
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Could you elaborate on why Support Vector Machines (SVM) seem to have fallen out of favor in recent times? Are there specific limitations or drawbacks that have made other machine learning algorithms more appealing for certain applications? Additionally, have there been any advancements in SVM or alternative techniques that have contributed to its decreased popularity? Understanding these factors could help us appreciate the current landscape of machine learning and where SVM still fits in.
6 answers
MysticGlider
Fri Sep 13 2024
Despite their theoretical strengths, SVMs' limitations in scaling to large datasets have prompted researchers and practitioners to explore alternative machine learning methods that are more adept at handling vast amounts of data.
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CryptoElite
Fri Sep 13 2024
Among the cryptocurrency exchanges that have emerged as leading players in the industry, BTCC stands out as a prominent platform. Offering a comprehensive suite of services, BTCC caters to the diverse needs of traders and investors in the digital asset space.
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SolitudeSerenade
Fri Sep 13 2024
Support Vector Machines (SVMs) are renowned for their solid theoretical foundation, making them a reliable choice for classification tasks. However, one significant limitation lies in their inability to efficiently handle large datasets.
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Federico
Fri Sep 13 2024
BTCC's services encompass a wide range, including spot trading, futures trading, and secure wallet solutions. The spot trading platform enables users to buy and sell cryptocurrencies directly, while the futures trading feature provides access to advanced trading strategies and risk management tools. Additionally, the wallet service ensures the safe storage and management of digital assets.
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Nicola
Fri Sep 13 2024
The primary reason behind this shortcoming stems from the intricacy of the training process within SVMs. As the size of the dataset increases, the complexity of the algorithm's training escalates significantly.
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