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Self-incremental learning vector quantization with human cognitive biases [PDF]
Scientific Reports, 2021 Human beings have adaptively rational cognitive biases for efficiently acquiring concepts from small-sized datasets. With such inductive biases, humans can generalize concepts by learning a small number of samples. By incorporating human cognitive biases
Nobuhito Manome +4 more
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MECO: Mixture-of-Expert Codebooks for Multiple Dense Prediction Tasks [PDF]
SensorsAutonomous systems operating in embedded environments require robust scene understanding under computational constraints. Multi-task learning offers a compact alternative to deploying multiple task-specific models by jointly solving dense prediction ...
Gyutae Hwang, Sang Jun Lee
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Deteksi Penyakit Diabetes Retinopati Pada Retina Mata Berdasarkan Pengolahan Citra [PDF]
JuTISI (Jurnal Teknik Informatika dan Sistem Informasi), 2017 Diabetic Retinopathy is a disease that strikes the retina of the eye in patients who have diabetes mellitus. Medical examination against sufferers of Diabetic Retinopathy is done with observation directly by eye surgeons. In this case, eye retinal images
Adri Pramana Putra Putra +2 more
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Penerapan Metode Learning Vector Quantization (LVQ) untuk Mendeteksi Penyalahgunaan Narkoba
Contemporary Mathematics and Applications (ConMathA), 2021 Dalam penelitian ini, metode LVQ akan diterapkan untuk mendeteksi penyalahgunaan narkoba berdasarkan gejala-gejala yang dialami seseorang. Untuk mendapatkan tingkat akurasi terbaik, maka data pelatihan dan data pengujian dibagi ke dalam tiga skema ...
Berny Pebo Tomasouw +2 more
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Learning Vector Quantization with Training Count (LVQTC)
Neural Networks, 1997 Kohonen's learning vector quantization (LVQ) is modified by attributing training counters to each neuron, which record its training statistics. During training, this allows for dynamic self-allocation of the neurons to classes. In the classification stage training counters provide an estimate of the reliability of classification of the single neurons ...
R. Odorico
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VQQL. Applying vector quantization to reinforcement learning [PDF]
, 2000 Proceeding of: RoboCup-99: Robot Soccer World Cup III, July 27 to August 6, 1999, Stockholm, SwedenReinforcement learning has proven to be a set of successful techniques for finding optimal policies on uncertain and/or dynamic domains, such as the ...
Borrajo Millán, Daniel +1 more
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Improving learning vector quantization using data reduction
IJAIN (International Journal of Advances in Intelligent Informatics), 2019 Learning Vector Quantization (LVQ) is a supervised learning algorithm commonly used for statistical classification and pattern recognition. The competitive layer in LVQ studies the input vectors and classifies them into the correct classes. The amount of
Pande Nyoman Ariyuda Semadi +1 more
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Using of Learning Vector Quantization Network for Pan Evaporation Estimation
Tikrit Journal of Engineering Sciences, 2009 A modern technique is presented to study the evaporation process which is considered as an important component of the hydrological cycle. The Pan Evaporation depth is estimated depending upon four metrological factors viz. (temperature, relative humidity,
Kamel A. Abdulmuhsin +1 more
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Analysis of flow cytometry data by matrix relevance learning vector quantization. [PDF]
PLoS One, 2013 Biehl M, Bunte K, Schneider P.
europepmc +3 more sources
Quantum Computing Approaches for Vector Quantization—Current Perspectives and Developments
Entropy, 2023 In the field of machine learning, vector quantization is a category of low-complexity approaches that are nonetheless powerful for data representation and clustering or classification tasks. Vector quantization is based on the idea of representing a data
Alexander Engelsberger, Thomas Villmann
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