NAME

       vw - Vowpal Wabbit -- fast online learning tool

DESCRIPTION

   VW options:
       --random_seed arg
              seed random number generator

       --ring_size arg
              size of example ring

   Update options:
       -l [ --learning_rate ] arg
              Set learning rate

       --power_t arg
              t power value

       --decay_learning_rate arg
              Set Decay factor for learning_rate between passes

       --initial_t arg
              initial t value

       --feature_mask arg
              Use  existing  regressor  to  determine  which parameters may be updated.  If no initial_regressor
              given, also used for initial weights.

   Weight options:
       -i [ --initial_regressor ] arg
              Initial regressor(s)

       --initial_weight arg
              Set all weights to an initial value of arg.

       --random_weights arg
              make initial weights random

       --input_feature_regularizer arg
              Per feature regularization input file

   Parallelization options:
       --span_server arg
              Location of server for setting up spanning tree

       --threads
              Enable multi-threading

       --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

   Diagnostic options:
       --version
              Version information

       -a [ --audit ]
              print weights of features

       -P [ --progress ] arg
              Progress update frequency. int: additive, float: multiplicative

       --quiet
              Don't output disgnostics and progress updates

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

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

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

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

       --redefine arg
              redefine namespaces beginning with characters of string S as namespace N.  <arg> shall be in  form
              'N:=S'  where := is operator. Empty N or S are treated as default namespace. Use ':' as a wildcard
              in S.

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

       --noconstant
              Don't add a constant feature

       -C [ --constant ] arg
              Set initial value of constant

       --ngram arg
              Generate N grams. To generate N grams for a single namespace 'foo', arg should be fN.

       --skips arg
              Generate skips in N grams. This in conjunction  with  the  ngram  tag  can  be  used  to  generate
              generalized n-skip-k-gram. To generate n-skips for a single namespace 'foo', arg should be fN.

       --feature_limit arg
              limit to N features. To apply to a single namespace 'foo', arg should be fN

       --affix arg
              generate  prefixes/suffixes  of features; argument '+2a,-3b,+1' means generate 2-char prefixes for
              namespace a, 3-char suffixes for b and 1 char prefixes for default namespace

       --spelling arg
              compute spelling features for a give namespace (use '_' for default namespace)

       --dictionary arg
              read a dictionary for additional features (arg either 'x:file' or just 'file')

       --dictionary_path arg
              look in this directory for dictionaries; defaults to current directory or env{PATH}

       --interactions arg
              Create feature interactions of any level between namespaces.

       --permutations
              Use permutations instead of combinations for feature interactions of same namespace.

       --leave_duplicate_interactions
              Don't remove interactions with duplicate combinations of namespaces.  For ex. this is a duplicate:
              '-q ab -q ba' and a lot more in '-q ::'.

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

       --q: arg
              : corresponds to a wildcard for all printable characters

       --cubic arg
              Create and use cubic features

   Example options:
       -t [ --testonly ]
              Ignore label information and just test

       --holdout_off
              no holdout data in multiple passes

       --holdout_period arg
              holdout period for test only, default 10

       --holdout_after arg
              holdout after n training examples, default off (disables holdout_period)

       --early_terminate arg
              Specify  the  number  of  passes  tolerated  when  holdout  loss  doesn't  decrease  before  early
              termination, default is 3

       --passes arg
              Number of Training Passes

       --initial_pass_length arg
              initial number of examples per pass

       --examples arg
              number of examples to parse

       --min_prediction arg
              Smallest prediction to output

       --max_prediction arg
              Largest prediction to output

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

       --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

       --l1 arg
              l_1 lambda

       --l2 arg
              l_2 lambda

       --named_labels arg
              use  names  for  labels  (multiclass, etc.)  rather than integers, argument specified all possible
              labels, comma-sep, eg "--named_labels Noun,Verb,Adj,Punc"

   Output model:
       -f [ --final_regressor ] arg
              Final regressor

       --readable_model arg
              Output human-readable final regressor with numeric features

       --invert_hash arg
              Output human-readable final regressor with feature names.  Computationally expensive.

       --save_resume
              save extra state so learning can be resumed later with new data

       --save_per_pass
              Save the model after every pass over data

       --output_feature_regularizer_binary arg
              Per feature regularization output file

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

   Output options:
       -p [ --predictions ] arg
              File to output predictions to

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

       Reduction options, use [option] --help for more info:

       --bootstrap arg
              k-way bootstrap by online importance resampling

       --search arg
              Use learning to search, argument=maximum action id or 0 for LDF

       --replay_c arg
              use experience replay at a  specified  level  [b=classification/regression,  m=multiclass,  c=cost
              sensitive] with specified buffer size

       --cbify arg
              Convert multiclass on <k> classes into a contextual bandit problem

       --cb_adf
              Do Contextual Bandit learning with multiline action dependent features.

       --cb arg
              Use contextual bandit learning with <k> costs

       --csoaa_ldf arg
              Use  one-against-all  multiclass  learning  with  label dependent features.  Specify singleline or
              multiline.

       --wap_ldf arg
              Use weighted all-pairs multiclass learning with label dependent features.

              Specify singleline or multiline.

       --interact arg
              Put weights on feature products from namespaces <n1> and <n2>

       --csoaa arg
              One-against-all multiclass with <k> costs

       --multilabel_oaa arg
              One-against-all multilabel with <k> labels

       --log_multi arg
              Use online tree for multiclass

       --ect arg
              Error correcting tournament with <k> labels

       --boosting arg
              Online boosting with <N> weak learners

       --oaa arg
              One-against-all multiclass with <k> labels

       --top arg
              top k recommendation

       --replay_m arg
              use experience replay at a  specified  level  [b=classification/regression,  m=multiclass,  c=cost
              sensitive] with specified buffer size

       --binary
              report loss as binary classification on -1,1

       --link arg (=identity)
              Specify the link function: identity, logistic or glf1

       --stage_poly
              use stagewise polynomial feature learning

       --lrqfa arg
              use low rank quadratic features with field aware weights

       --lrq arg
              use low rank quadratic features

       --autolink arg
              create link function with polynomial d

       --new_mf arg
              rank for reduction-based matrix factorization

       --nn arg
              Sigmoidal feedforward network with <k> hidden units

       --confidence
              Get confidence for binary predictions

       --active_cover
              enable active learning with cover

       --active
              enable active learning

       --replay_b arg
              use  experience  replay  at  a  specified level [b=classification/regression, m=multiclass, c=cost
              sensitive] with specified buffer size

       --bfgs use bfgs optimization

       --conjugate_gradient
              use conjugate gradient based optimization

       --lda arg
              Run lda with <int> topics

       --noop do no learning

       --print
              print examples

       --rank arg
              rank for matrix factorization.

       --sendto arg
              send examples to <host>

       --svrg Streaming Stochastic Variance Reduced Gradient

       --ftrl FTRL: Follow the Proximal Regularized Leader

       --pistol
              FTRL: Parameter-free Stochastic Learning

       --ksvm kernel svm

   Gradient Descent options:
       --sgd  use regular stochastic gradient descent update.

       --adaptive
              use adaptive, individual learning rates.

       --invariant
              use safe/importance aware updates.

       --normalized
              use per feature normalized updates

       --sparse_l2 arg (=0)
              use per feature normalized updates

   Input options:
       -d [ --data ] arg
              Example Set

       --daemon
              persistent daemon mode on port 26542

       --port arg
              port to listen on; use 0 to pick unused port

       --num_children arg
              number of children for persistent daemon mode

       --pid_file arg
              Write pid file in persistent daemon mode

       --port_file arg
              Write port used in persistent daemon mode

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

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

       -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.   A  mixture  of  raw-text  &  compressed  inputs  are  supported  with
              autodetection.

       --no_stdin
              do not default to reading from stdin

vw 8.1.1                                            May 2014                                               VW(1)