Method and system for training and neural network models for large number of discrete features for information rertieval
AssigneeBaidu USA LLC
InventorShuang Wu
Life of this patent
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Abstract
According to one embodiment, a first set of features is received, where each of the features in the first set being associated with a predetermined category. A bloom filter is applied to the first set of features to generate a second set of features. A neural network model is trained by applying the second set of features to a first layer of nodes of the neural network model to generate an output, the neural network model including a plurality of layers of nodes coupled to each other via a connection. The output of the neural network model is compared with a target value associated with the predetermined category to determine whether the neural network model satisfies a predetermined condition.
Claims24
A computer-implemented method for generating neural network models, the method comprising:
- receiving a first set of features, each of the features in the first set being associated with a predetermined category;
- applying a bloom filter to the first set of features to generate a second set of features;
- training a neural network model by applying the second set of features to a first layer of nodes of the neural network model to generate an output, the neural network model including a plurality of layers of nodes coupled to each other via a connection;
- and
- comparing the output of the neural network model with a target value associated with the predetermined category to determine whether the neural network model satisfies a predetermined condition.
A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations of training classification models, the operations comprising:
- receiving a first set of features, each of the features in the first set being associated with a predetermined category;
- applying a bloom filter to the first set of features to generate a second set of features;
- training a neural network model by applying the second set of features to a first layer of nodes of the neural network model to generate an output, the neural network model including a plurality of layers of nodes coupled to each other via a connection;
- and
- comparing the output of the neural network model with a target value associated with the predetermined category to determine whether the neural network model satisfies a predetermined condition.
A data processing system, comprising:
- a processor;
- and
- a memory coupled to the processor, the memory storing instructions, which when executed by the processor, cause the processor to perform operations of training classification models, the operations including
- receiving a first set of features, each of the features in the first set being associated with a predetermined category,
- applying a bloom filter to the first set of features to generate a second set of features,
- training a neural network model by applying the second set of features to a first layer of nodes of the neural network model to generate an output, the neural network model including a plurality of layers of nodes coupled to each other via a connection, and
- comparing the output of the neural network model with a target value associated with the predetermined category to determine whether the neural network model satisfies a predetermined condition.
Dependencies are inferred from the claim text.