What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Artificial intelligence, Pattern recognition, Statistics, Kernel regression and Radial basis function.
He works mostly in the field of Artificial intelligence, limiting it down to topics relating to Machine learning and, in certain cases, Bayesian probability, as a part of the same area of interest.
His research integrates issues of Image processing and Numeral system in his study of Pattern recognition.
His Statistics research is multidisciplinary, incorporating elements of Bounded function and Applied mathematics.
Adam Krzyżak combines subjects such as Rate of convergence, Pointwise, Random variable and Control theory with his study of Kernel regression.
His Handwriting recognition study integrates concerns from other disciplines, such as Segmentation, Curvature, Classifier and Binary image.
His most cited work include:
- Methods of combining multiple classifiers and their applications to handwriting recognition (2049 citations)
- Rival penalized competitive learning for clustering analysis, RBF net, and curve detection (568 citations)
- Learning and design of principal curves (294 citations)
What are the main themes of his work throughout his whole career to date?
Adam Krzyżak spends much of his time researching Artificial intelligence, Pattern recognition, Applied mathematics, Statistics and Rate of convergence.
His Artificial intelligence research includes themes of Machine learning and Computer vision.
Adam Krzyżak regularly ties together related areas like Numeral system in his Pattern recognition studies.
Adam Krzyżak has included themes like Nonparametric statistics, Radial basis function, Regression, Bounded function and Nonlinear system in his Applied mathematics study.
His Rate of convergence research is multidisciplinary, relying on both Smoothing, Estimator and Mathematical optimization.
His work deals with themes such as Algorithm and Radial basis function network, which intersect with Mathematical optimization.
He most often published in these fields:
- Artificial intelligence (44.02%)
- Pattern recognition (29.34%)
- Applied mathematics (18.15%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (44.02%)
- Pattern recognition (29.34%)
- Artificial neural network (13.90%)
In recent papers he was focusing on the following fields of study:
Adam Krzyżak mainly focuses on Artificial intelligence, Pattern recognition, Artificial neural network, Rate of convergence and Deep learning.
He has researched Artificial intelligence in several fields, including Logarithm and Nonparametric regression.
He merges Pattern recognition with Epileptic seizure in his research.
The various areas that Adam Krzyżak examines in his Artificial neural network study include Transfer of learning, Paraphrase, Natural language processing and Breast cancer.
His Rate of convergence research incorporates elements of Uncertainty quantification, Density estimation, Imperfect, Mathematical optimization and Function.
His Classifier study combines topics from a wide range of disciplines, such as Computational complexity theory, Data decomposition, Boosting and Support vector machine.
Between 2016 and 2021, his most popular works were:
- Automatic Epileptic Seizure Detection in EEG Using Nonsubsampled Wavelet–Fourier Features (31 citations)
- Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning. (25 citations)
- Estimation of a function of low local dimensionality by deep neural networks (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Artificial intelligence, Data mining, Pattern recognition, Rate of convergence and Algorithm.
His Artificial intelligence research incorporates themes from Nonparametric regression and Least squares.
His Data mining research is multidisciplinary, incorporating perspectives in Silhouette, Validity assessment and Cluster analysis.
Adam Krzyżak undertakes interdisciplinary study in the fields of Pattern recognition and Epileptic seizure through his works.
His Rate of convergence course of study focuses on Function and Quantile, Statistics, Importance sampling, Combinatorics and Random variable.
His research in Algorithm intersects with topics in Classifier, Support vector machine and Decision tree.
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