search for: training_set_filtered

...ss training.filter <- filter.const(training[,-1]) # The filtered data frame is training.filtered <- cbind(myclass=training[,1], training.filter$x) dim(training.filtered) # => 49 rows and 250 columns # Save the filtered training set for later use in classification filtered.data <- 'training_set_filtered.Rdata' save(training.filtered, file=filtered.data) #----------------- # THE FOLLOWING FILTERING STEP TAKES 3 HOUR ON MY PowerBook # AND CONSUMES ABOUT 600 Mb MEMORY. # # I WOULD BE HAPPY ABOUT ANY HINT HOW TO IMPROVE THIS. # Pre-filter the big data set (more than 115,000 rows and 524 column...