We have developed a method for estimating the orbital periods of dwarf novae from the Sloan Digital Sky Survey (SDSS) colors in quiescence using an artificial neural network. For typical objects below the period gap with sufficient photometric accuracy, we were able to estimate the orbital periods with accuracy to a 1{sigma} error of 22%. The error of the estimation is worse for systems with longer orbital periods. We have also developed a neural-network-based method for categorical classification. This method has proven to be efficient in classifying objects into three categories (WZ Sge type, SU UMa type, and SS Cyg/Z Cam type), and works for very faint objects to a limit of g=21mag. Using this method, we have investigated the distribution of the orbital periods of dwarf novae from a modern transient survey (Catalina Real-Time Survey).

Cone search capability for table J/PASJ/64/63/dnovae (List of dwarf novae, estimated extinction, and neural-network classification)