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Journal of Machine Learning Research 2 (2001) 139-154 Submitted 3/01; Published 12/01 One-Class Sums for Document Classification Larry M. Magnetite Malik Yousef Department of Computer Science University
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How to fill out one class svms for

How to fill out one class SVMS for:
01
First, gather labeled data for the class you want to classify. This data should include examples of both positive and negative instances of the class.
02
Preprocess the data by cleaning and normalizing it. This may involve removing noise, handling missing values, or scaling the features.
03
Split the data into a training set and a testing set. The training set will be used to train the SVM model, while the testing set will be used to evaluate its performance.
04
Choose the appropriate kernel function for the SVM. This function maps the input data into a higher-dimensional space where it can be linearly separated.
05
Train the SVM model using the training set and the chosen kernel function. This involves solving an optimization problem to find the optimal hyperplane that maximally separates the positive and negative instances.
06
Tune the hyperparameters of the SVM model to improve its performance. This may involve adjusting the regularization parameter or the kernel parameters.
07
Evaluate the trained SVM model using the testing set. Calculate metrics such as accuracy, precision, and recall to assess its performance.
Who needs one class SVMS for:
01
Data scientists and machine learning practitioners who want to solve binary classification problems where only one class is of interest.
02
Anomaly detection tasks where the goal is to identify instances that deviate significantly from the norm.
03
Fraud detection applications where the objective is to detect fraudulent transactions or activities in a dataset.
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