VW(1)								   User Commands							     VW(1)

NAME

       vw - Vowpal Wabbit -- fast online learning tool

DESCRIPTION

   VW options:
       -h [ --help ]
	      Look here: http://hunch.net/~vw/ and click on Tutorial.

       --active_learning
	      active learning mode

       --active_simulation
	      active learning simulation mode

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

       --adaptive
	      use adaptive, individual learning rates.

       --exact_adaptive_norm
	      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

       --bfgs use bfgs optimization

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

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

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

       --conjugate_gradient
	      use conjugate gradient based optimization

       --nonormalize
	      Do not normalize online updates

       --l1 arg (=0)
	      l_1 lambda

       --l2 arg (=0)
	      l_2 lambda

       -d [ --data ] arg
	      Example Set

       --daemon
	      persistent daemon mode on port 26542

       --num_children arg (=10)
	      number of children for persistent daemon mode

       --pid_file arg
	      Write pid file in persistent daemon mode

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

       --input_feature_regularizer arg
	      Per feature regularization input file

       -f [ --final_regressor ] arg
	      Final regressor

       --readable_model arg
	      Output human-readable final regressor

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

       --hessian_on
	      use second derivative in line search

       --version
	      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

       --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

       --span_server arg
	      Location of server for setting up spanning tree

       --min_prediction arg
	      Smallest prediction to output

       --max_prediction arg
	      Largest prediction to output

       --mem arg (=15)
	      memory in bfgs

       --noconstant
	      Don't add a constant feature

       --noop do no learning

       --output_feature_regularizer_binary arg
	      Per feature regularization output file

       --output_feature_regularizer_text arg Per feature regularization output file,
	      in text

       --port arg
	      port to listen on

       --power_t arg (=0.5)
	      t power value

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

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

       --termination arg (=0.00100000005)
	      Termination threshold

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

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

       --quiet
	      Don't output diagnostics

       --rank arg (=0)
	      rank for matrix factorization.

       --random_weights arg
	      make initial weights random

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

       --save_per_pass
	      Save the model after every pass over data

       --sendto arg
	      send examples to <host>

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

       --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 	au associated with Quantile loss. Defaults to 0.5

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

       --total arg (=1)
	      total number of nodes used in cluster parallel job

       --node arg (=0)
	      node number in cluster parallel job

       --sort_features
	      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.

vw 6.1								     June 2012								     VW(1)