Abstract: An apparatus and method for analyzing time series data on the basis of machine learning are provided. According to the disclosed embodiments, it is possible to effectively augment time series data, which is a target to be learned, according to characteristics of the time series data, thereby solving a problem of overfitting a machine learning model due to limited training data and a problem of deterioration of prediction accuracy due to imbalance of distribution of time series data and improving reliability of time series data analysis. In addition, according to the disclosed embodiments, it is possible to effectively set an optimal parameter for augmenting time series data.

Abstract: A confidence measurement method according to an embodiment includes additionally training a feature extractor of a classification model trained using training data including a plurality of images, using the training data, such that feature vectors for images labeled with the same class among feature vectors for the plurality of images become closer to each other in an embedding space for the feature extractor, and measuring confidence of a classification result for an input image by the trained classification model using the additionally trained feature extractor.

Abstract: A confidence measurement method according to an embodiment includes additionally training a feature extractor of a classification model trained using training data including a plurality of images, using the training data, such that feature vectors for images labeled with the same class among feature vectors for the plurality of images become closer to each other in an embedding space for the feature extractor, and measuring confidence of a classification result for an input image by the trained classification model using the additionally trained feature extractor.

Abstract: Provided is a speech recognition method for a recognition target language. According to an embodiment of the inventive concept, a speech recognition method for a recognition target language performed by a speech recognition apparatus includes obtaining an original learning data set for the recognition target language, constructing a target label by dividing the text information included in each piece of original learning data in letter units, and building an acoustic model based on a deep neural network by learning the learning speech data included in the each piece of original learning data and the target label corresponding to the learning speech data.

Abstract: An apparatus and method for analyzing time series data on the basis of machine learning are provided. According to the disclosed embodiments, it is possible to effectively augment time series data, which is a target to be learned, according to characteristics of the time series data, thereby solving a problem of overfitting a machine learning model due to limited training data and a problem of deterioration of prediction accuracy due to imbalance of distribution of time series data and improving reliability of time series data analysis. In addition, according to the disclosed embodiments, it is possible to effectively set an optimal parameter for augmenting time series data.

Abstract: Provided is a speech recognition method for a recognition target language. According to an embodiment of the inventive concept, a speech recognition method for a recognition target language performed by a speech recognition apparatus includes obtaining an original learning data set for the recognition target language, constructing a target label by dividing the text information included in each piece of original learning data in letter units, and building an acoustic model based on a deep neural network by learning the learning speech data included in the each piece of original learning data and the target label corresponding to the learning speech data.

Abstract: A voice recognition system and method are provided. The voice recognition system includes a voice input unit configured to receive learning voice data and a target label including consonant and vowel (letter) information representing the learning voice data, and divide the learning voice data into windows having a predetermined size; a first voice recognition unit configured to learn features of the divided windows using a first neural network model and the target label; a second voice recognition unit configured to learn a time-series pattern of the extracted features using a second neural network model; and a text output unit configured to convert target voice data input to the voice input unit into a text based on learning results of the first voice recognition unit and the second voice recognition unit, and output the text.

Abstract: A voice recognition system and method are provided. The voice recognition system includes a voice input unit configured to receive learning voice data and a target label including consonant and vowel (letter) information representing the learning voice data, and divide the learning voice data into windows having a predetermined size; a first voice recognition unit configured to learn features of the divided windows using a first neural network model and the target label; a second voice recognition unit configured to learn a time-series pattern of the extracted features using a second neural network model; and a text output unit configured to convert target voice data input to the voice input unit into a text based on learning results of the first voice recognition unit and the second voice recognition unit, and output the text.

Abstract: Disclosed herein are an apparatus for generating a prediction model and a method thereof. The apparatus for generating a prediction model from data composed of a plurality of instances each including one or more predictor values and a target value includes a pre-processing module configured to generate pre-processed target values by calculating weighted averages of the target values for a predetermined prediction period and subtracting the weighted averages from the target values, a prediction model generation module configured to calculate prediction values of the target values of the respective instances from the plurality of instances including the pre-processed target values, and a post-processing module configured to add the weighted averages, which are subtracted in the pre-processing module, to the prediction values of the target values of the respective instances.

Abstract: An apparatus and a method for data analysis and prediction, the apparatus including: a data collection unit configured to collect a prediction value derived through a machine learning of input data; a candidate prediction value generation unit configured to generate a candidate prediction value; a rule verification value configured to store one or more rules and verify whether the candidate prediction value violates the one or more rules; and an evaluation unit configured to calculate a fitness of the candidate prediction value according to an error rate of the candidate prediction value with respect to the prediction value and a verification result of the rule verification unit.