I’m kinda stuck with Support Vector Machines (SVMs). I once used it for a simple image classification project and it worked like a charm. But now, I’m trying to apply it to a more complex dataset, and it’s just not cutting it.
I’m getting weird results, and I’m not sure if it’s my data, the kernel I’m using, or if I’m missing something completely obvious. Any tips or advice would be greatly appreciated
I’ve been in a similar situation with Support Vector Machines (SVMs). While they worked well for a simpler image classification task, I found them less effective with more complex datasets. From my experience, the issue could be related to the choice of kernel, the scale of your features, or the quality of your data. It’s worth experimenting with different kernels, like polynomial or radial basis function (RBF), and ensuring your data is properly scaled. Also, double-check for any anomalies or noise in your dataset. Fine-tuning hyperparameters and cross-validation can also help in improving performance.
Have you tried different SVM implementations or libraries?
Are there any error messages or warnings that might be relevant?
Have you considered exploring other machine learning algorithms as potential alternatives?