On Thu, 28 Jan 2010 11:55:48 -0500
Roman Gelfand <[email protected]> wrote:
> #
> # Training Mode: The default training mode to use for all operations, when
> # one has not been specified on the commandline or in the user's preferences.
> # Acceptable values are:
> # toe Train on Error (Only)
> # teft Train Everything (Trains on every message)
> # tum Train Until Mature (Train only tokens without enough data)
> # notrain Do not train or store signatures (large ISP systems, post-train)
> #
> TrainingMode teft
>
Please switch that to "toe"! Using "teft" is old school and one part of your
problem.
> #
> # Features: Specify features to activate by default; can also be specified
> # on the commandline. See the documentation for a list of available features.
> # If _any_ features are specified on the commandline, these are ignored.
> #
> #Feature noise
> Feature whitelist
>
Enable "noise". It's a good thing that will help you.
> # Training Buffer: The training buffer waters down statistics during training.
> # It is designed to prevent false positives, but can also dramatically reduce
> # dspam's catch rate during initial training. This can be a number from 0
> # (no buffering) to 10 (maximum buffering). If you are paranoid about false
> # positives, you should probably enable this option.
> #
> #Feature tb=5
>
Depending on the data you already have learned, it could be beneficial to
enable this option.
> #
> # Tokenizer: Specify the tokenizer to use. The tokenizer is the piece
> # responsible for parsing the message into individual tokens. Depending on
> # how many resources you are willing to trade off vs. accuracy, you may
> # choose to use a less or more detailed tokenizer:
> # word uniGram (single word) tokenizer
> # Tokenizes message into single individual words/tokens
> # example: "free" and "viagra"
> # chain biGram (chained tokens) tokenizer (default)
> # Single words + chains adjacent tokens together
> # example: "free" and "viagra" and "free viagra"
> # sbph Sparse Binary Polynomial Hashing tokenizer
> # Creates sparse token patterns across sliding window of 5-tokens
> # example: "the quick * fox jumped" and "the * * fox jumped"
> # osb Orthogonal Sparse biGram tokenizer
> # Similar to SBPH, but only uses the biGrams
> # example: "the * * fox" and "the * * * jumped"
> #
> Tokenizer chain
>
That is the main part of your problem. It is no surprise that you retrain and
retrain and retrain and still don't get the data to flip the state. Please use
"osb". It's way better for your situation.
> #
> # Preferences: Specify any preferences to set by default, unless otherwise
> # overridden by the user (see next section) or a default.prefs file.
> # If user or default.prefs are found, the user's preferences will override any
> # defaults.
> #
> Preference "trainingMode=TEFT" # { TOE | TUM | TEFT | NOTRAIN
> } -> default:teft
>
Set this to "TOE"
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