5 results about "Pseudo data" patented technology

The information processing device 1X is an AI based device to support a decision making and includes an acquisition means 20X, a calculation means 22X, a selection means 23X, and a generation means 24X. The acquisition means 20X is configured to acquire a plurality of sets each including a sample and a label. The calculation means 22X is configured to calculate, for each of the plurality of sets, accuracy of the label based on a difference between the label and a predicted result of the label predicted from the sample paired with the label. The selection means 23X is configured to select a set to be used for generating pseudo data from the plurality of sets, based on the accuracy. The generation means 24X is configured to generate the pseudo data, based on the selected set.

This invention discloses a method for generating tabular data based on local context retrieval and fine-tuning, comprising: obtaining raw tabular data samples for machine learning training from a public benchmark database, performing preprocessing and data partitioning to obtain feature columns and label columns, and constructing a training set; fine-tuning the local calibration parameters of a TabPFN model based on the training set to obtain a parameter-fine-tuned TabPFN model; obtaining pseudo-data samples based on the training set by manually setting the target number of samples and target category labels; and iteratively updating the pseudo-data samples and the parameter-fine-tuned TabPFN model using an iterative sampling method based on dynamic local context to finally obtain a tabular dataset. This invention, through local calibration parameter fine-tuning and dynamic local context iterative sampling, can generate high-quality, diverse tabular datasets, effectively improving the realism and usability of the generated data.

The objective is to improve the accuracy of the data on the number of abnormal packets generated. [Solution] The communication management device 1 includes a generator 131 that generates pseudo-data statistically similar to true data, using each of the sequences of packet counts observed for each time period corresponding to the second data as true data, and a discriminator 132 that identifies whether the pseudo-data generated by the generator 131 belongs to the class corresponding to true data or the class corresponding to pseudo-data. The third learning unit 13 is configured to learn multiple generative models, each having an objective function set with the probability distribution of the second data and the probability distribution of the first data determined from the ratio estimated by the first learning unit 11, and the second parameter estimated by the second learning unit 12.