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There is no single rule for the best learning rate form. sample, typically and are searched in a logarithmic scale.Very often, the defaults are reasonable and only the -l option () needs to be explored.For other problems the defaults may be inadequate, e.g. To specify a loss function use -b [ --bit_precision ] arg number of bits in the feature table -i [ --initial_regressor ] arg Initial regressor(s) to load into memory (arg is filename) -f [ --final_regressor ] arg Final regressor to save (arg is filename) --random_weights arg make initial weights random --sparse_weights Use a sparse datastructure for weights --initial_weight arg (=0) Set all weights to initial value of arg --readable_model arg Output human-readable final regressor --invert_hash arg Output human-readable final regressor with feature names --audit_regressor arg stores feature names and their regressor values.
-k [ --kill_cache ] do not reuse existing cache: create a new one always --compressed use gzip format whenever possible.
If a cache file is being created, this option creates a compressed cache file.
For details the relevant papers are Adaptive Bound Optimization for Online Convex Optimization and Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
These learning rates give an improvement when the data have many features, but they can be slightly slower especially when used in conjunction with options that cause examples to have many non-zero features such as is a trick to make LBFGS quasi-online.
Thus, the second step may be used on any dataset that contains the same features.
It cannot process features that have hash collisions - the first one encountered will be printed out and the others ignored.
-d [ --data ] arg Example Set --ring_size arg size of example ring --examples arg number of examples to parse --daemon read data from port 26542 --port arg port to listen on --num_children arg (=10) number of children for persistent daemon mode --pid_file arg Write pid file in persistent daemon mode --passes arg (=1) Number of Training Passes -c [ --cache ] Use a cache.
The default is .cache --cache_file arg The location(s) of cache_file.
Instead of using the inverse hessian approximation directly, you compute a second derivative in the update direction and use that to compute the step size via a parabolic approximation.Tags: Adult Dating, affair dating, sex dating