Why svms are not ideal for multiclass problems?
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Support Vector Machines (SVMs) are not ideal for multiclass problems because they were originally designed for binary classification tasks . While SVMs can be extended to handle multiclass problems using techniques like one-versus-one (OvO) or one-versus-all (OvA), these approaches can lead to regions of inconclusive predictions . Additionally, SVMs have parameters that need to be carefully tuned for optimal performance, which can be challenging in multiclass scenarios . Furthermore, the time complexity of SVMs makes them impractical for training on large datasets with millions of data points . To address these limitations, alternative approaches such as multilevel SVMs have been developed, which build a hierarchy of problems and train SVM models for each level, resulting in faster training on huge datasets .
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| Papers (4) | Insight |
|---|---|
| SVMs are not ideal for multiclass problems because they can lead to inconclusive predictions and require multiple binary SVMs to be applied repeatedly. | |
03 Jul 2023 | The provided paper does not mention anything about SVMs or their suitability for multiclass problems. |
| The provided paper does not mention why SVMs are not ideal for multiclass problems. | |
| The paper does not provide an answer to why SVMs are not ideal for multiclass problems. |
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