Check emails using a neural network algorithm for better spam detection using a trained AI model.
Neural network data directory - neuralnetwork_data_dir
Where the NeuralNetwork plugin will store its data.
Default: /etc/mail/spamassassin/.neuralnetwork
Neural network min text length - neuralnetwork_min_text_len
The minimum number of characters of visible text required to run prediction or learning on a message.
Default: 256
Neural network min word length - neuralnetwork_min_word_len
The minimum token length considered when building the vocabulary and feature vectors.
Default: 4
Neural network max word length - neuralnetwork_max_word_len
The maximum token length considered when building the vocabulary and feature vectors.
Default: 24
Neural network vocab cap - neuralnetwork_vocab_cap
The maximum number of vocabulary terms to retain; least-frequent terms are pruned when exceeded.
Default: 10000
Neural network min vocab hits - neuralnetwork_min_vocab_hits
The minimum number of tokens in the email that must exist in the vocabulary for prediction to run.
Default: 10
Neural network cache TTL - neuralnetwork_cache_ttl
The time-to-live in seconds for the in-memory vocabulary and model caches Set to 0 to disable caching.
Default: 300
Neural network min spam count - neuralnetwork_min_spam_count
The minimum number of spam messages in the vocabulary required to enable prediction.
Default: 100
Neural network min ham count - neuralnetwork_min_ham_count
The minimum number of ham messages in the vocabulary required to enable prediction.
Default: 100
neuralnetwork_spam_threshold - neuralnetwork_spam_threshold
Prediction values above this threshold are considered spam.
Default: 0.8
Neural network ham threshhold - neuralnetwork_ham_threshold
Prediction values below this threshold are considered ham.
Default: 0.2
Neural network learning rate - neuralnetwork_learning_rate
The learning rate used by the underlying FANN network during incremental training.
Default: 0.1
Neural network momentum - neuralnetwork_momentum
The momentum used for training updates.
Default: 0.1
Neural network train epochs - neuralnetwork_train_epochs
The number of training epochs to perform when learning a single message.
Default: 50
Neural network train algorithm - neuralnetwork_train_algorithm
The algorithm used by Fann neural network used when training, might increase speed depending on the data volume.
Default: FANN_TRAIN_RPROP
Neural network lock timeout - neuralnetwork_lock_timeout
The maximum number of seconds to wait for the exclusive training lock before giving up and skipping the learn operation.
Set to 0 to wait indefinitely.
Default: 10
neuralnetwork_stopwords - neuralnetwork_stopwords
A space-separated list of stopwords to ignore when tokenizing text.
Default: the and for with that this from there their have be not but you your
Neural network autolearn - neuralnetwork_autolearn
When SpamAssassin declares a message spam or ham during the message scan, and launches the auto-learn process, the
message is autolearned as spam/ham in the same way as during the manual learning.
Default: 0