Provided by: vowpal-wabbit_5.1+83-gffab10a-1_amd64 bug


       vw - Vowpal Wabbit -- fast online learning tool


   VW options:
              active learning mode

              active learning simulation mode

       --active_mellowness arg (=8)
              active learning mellowness parameter c_0. Default 8

              use adaptive, individual learning rates.

              use a more expensive exact norm for adaptive learning rates.

       -a [ --audit ]
              print weights of features

       -b [ --bit_precision ] arg
              number of bits in the feature table

              turn on delayed backprop

       -c [ --cache ]
              Use a cache.  The default is <data>.cache

       --cache_file arg
              The location(s) of cache_file.

              use  gzip  format  whenever  appropriate.   If  a cache file is being created, this
              option creates a compressed cache file.  A mixture of raw-text & compressed  inputs
              are supported if this option is on

              use conjugate gradient based optimization

       --regularization arg (=1)
              l_2 regularization for conjugate_gradient

              turn on corrective updates

       -d [ --data ] arg
              Example Set

              read data from port 39523

       --decay_learning_rate arg (=1)
              Set Decay factor for learning_rate between passes

       -f [ --final_regressor ] arg
              Final regressor

       --readable_model arg
              Output human-readable final regressor

       --global_multiplier arg (=1)
              Global update multiplier

              Do delayed global updates

       --hash arg
              how to hash the features. Available options: strings, all

       -h [ --help ]
              Output Arguments

              Version information

       --ignore arg
              ignore namespaces beginning with character <arg>

       --initial_weight arg (=0)
              Set all weights to an initial value of 1.

       -i [ --initial_regressor ] arg
              Initial regressor(s)

       --initial_pass_length arg (=18446744073709551615)
              initial number of examples per pass

       --initial_t arg (=1)
              initial t value

       --l1 arg (=0)
              l_1 regularization level

       --lda arg
              Run lda with <int> topics

       --lda_alpha arg (=0.100000001)
              Prior on sparsity of per-document topic weights

       --lda_rho arg (=0.100000001)
              Prior on sparsity of topic distributions

       --lda_D arg (=10000)
              Number of documents

       --minibatch arg (=1)
              Minibatch size, for LDA

       --master_location arg
              Location of master for setting up spanning tree

       --min_prediction arg
              Smallest prediction to output

       --max_prediction arg
              Largest prediction to output

       --multisource arg
              multiple sources for daemon input

       --noop do no learning

       --port arg
              port to listen on

       --power_t arg (=0.5)
              t power value

       --predictto arg
              host to send predictions to

       -l [ --learning_rate ] arg (=10)
              Set Learning Rate

       --passes arg (=1)
              Number of Training Passes

       -p [ --predictions ] arg
              File to output predictions to

       -q [ --quadratic ] arg
              Create and use quadratic features

              Don't output diagnostics

       --random_weights arg
              make initial weights random

       -r [ --raw_predictions ] arg
              File to output unnormalized predictions to

       --sendto arg
              send example to <hosts>

       -t [ --testonly ]
              Ignore label information and just test

       --thread_bits arg (=0)
              log_2 threads

       --loss_function arg (=squared)
              Specify  the loss function to be used, uses squared by default. Currently available
              ones are squared, classic, hinge, logistic and quantile.

       --quantile_tau arg (=0.5)
              Parameter \tau associated with Quantile loss. Defaults to 0.5

       --unique_id arg (=0)
              unique id used for cluster parallel

              turn this on to disregard order in which features have been defined. This will lead
              to smaller cache sizes

       --ngram arg
              Generate N grams

       --skips arg
              Generate  skips  in  N grams. This in conjunction with the ngram tag can be used to
              generate generalized n-skip-k-gram.