  As machine learning algorithms become increasingly accessible, a growing
number of organizations and researchers are using these technologies to
automate the process of exoplanet detection. These mainly utilize Convolutional
Neural Networks (CNNs) to detect periodic dips in lightcurve data. While having
approximately 5% lower accuracy than CNNs, the results of this study show that
One-Class Support Vector Machines (SVMs) can be fitted to data up to 84 times
faster than simple CNNs and make predictions over 3 times faster on the same
datasets using the same hardware. In addition, One-Class SVMs can be run
smoothly on unspecialized hardware, removing the need for Graphics Processing
Unit (GPU) usage. In cases where time and processing power are valuable
resources, One-Class SVMs are able to minimize time spent on transit detection
tasks while maximizing performance and efficiency.
