{ "id": "d964d695c5019dd3a12deea7185b9e9a75b94cc4fdfbde9dc19b1a23d0dd90fb", "pubkey": "dd664d5e4016433a8cd69f005ae1480804351789b59de5af06276de65633d319", "created_at": 1706567648, "kind": 30041, "tags": [ [ "title", "Zetteltest" ], [ "d", "au2cmwllx7l7to43" ] ], "content": "\nOne popular use case for SVM is text classification. SVM can be used to classify text documents into predefined categories, such as sentiment analysis (positive, negative, or neutral) or topic classification (sports, politics, entertainment, etc.). SVMs are particularly useful when dealing with high-dimensional data like text, where the number of features can be significantly larger than the number of training samples. By representing text documents in a numerical feature space, SVMs can effectively separate different classes and achieve high accuracy in classification.\n\n", "sig": "d25b338a8f48cf3f5f873deb95cbd406827e5a3ade876e6b979603234a743b4e87a0e43f7d1e672a575d9b7a4a81f81438ab3c697e75130e72d94bc0d757b8d9" }