On pattern classification algorithms - Introduction and survey

Pattern recognition algorithms, and mathematical techniques of estimation, decision making, and optimization theor

Practical Applications of Sequential Pattern Recognition Techniques

Experiments involving sequential recognition techniques and feature ordering schemes were performed on 23 feature samples of vowel spectra and 12 feature samples of remotely sensed agricultural crop data. Since each experiment dealt with two pattern classes, Wald\u27s sequential probability ratio test was used. The test was implemented with both fixed and time-varying stopping boundaries. Feature ordering was accomplished by both dispersion analysis and the divergence criterion

Practical Applications of Sequential Pattern Recognition Techniques

Experiments involving sequential recognition techniques and feature ordering schemes were performed on 23 feature samples of vowel spectra and 12 feature samples of remotely sensed agricultural crop data. Since each experiment dealt with two pattern classes, Wald\u27s sequential probability ratio test was used. The test was implemented with both fixed and time-varying stopping boundaries. Feature ordering was accomplished by both dispersion analysis and the divergence criterion

Polyphonic audio tagging with sequentially labelled data using CRNN with learnable gated linear units

Audio tagging aims to detect the types of sound events occurring in an audio recording. To tag the polyphonic audio recordings, we propose to use Connectionist Temporal Classification (CTC) loss function on the top of Convolutional Recurrent Neural Network (CRNN) with learnable Gated Linear Units (GLU-CTC), based on a new type of audio label data: Sequentially Labelled Data (SLD). In GLU-CTC, CTC objective function maps the frame-level probability of labels to clip-level probability of labels. To compare the mapping ability of GLU-CTC for sound events, we train a CRNN with GLU based on Global Max Pooling (GLU-GMP) and a CRNN with GLU based on Global Average Pooling (GLU-GAP). And we also compare the proposed GLU-CTC system with the baseline system, which is a CRNN trained using CTC loss function without GLU. The experiments show that the GLU-CTC achieves an Area Under Curve (AUC) score of 0.882 in audio tagging, outperforming the GLU-GMP of 0.803, GLU-GAP of 0.766 and baseline system of 0.837. That means based on the same CRNN model with GLU, the performance of CTC mapping is better than the GMP and GAP mapping. Given both based on the CTC mapping, the CRNN with GLU outperforms the CRNN without GLU.Comment: DCASE2018 Workshop. arXiv admin note: text overlap with arXiv:1808.0193

Research on the utilization of pattern recognition techniques to identify and classify objects in video data Technical progress report, 31 Jan. - 31 May 1967

Pattern recognition techniques for extracting information from video data and for reducing amount of data to convey this information - decision mechanisms and property filter