What is she best known for?
The fields of study she is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Her primary scientific interests are in Artificial intelligence, Pattern recognition, Data mining, Wavelet and Algorithm.
Her studies deal with areas such as Univariate and Calibration, Machine learning, Linear model, Least squares as well as Artificial intelligence.
Her Pattern recognition research integrates issues from Statistics and Identification.
Her studies in Data mining integrate themes in fields like Projection pursuit, Missing data, Spatial clustering and Outlier.
Her Algorithm study combines topics in areas such as Dynamic time warping and Plot.
Her work is dedicated to discovering how Feature selection, Multivariate statistics are connected with Principal component analysis, Cluster analysis, Selection and Knowledge extraction and other disciplines.
Her most cited work include:
- Particle swarm optimization (PSO). A tutorial (319 citations)
- Rough sets theory (305 citations)
- Robust statistics in data analysis — A review: Basic concepts (232 citations)
What are the main themes of her work throughout her whole career to date?
Beata Walczak focuses on Artificial intelligence, Pattern recognition, Chromatography, Data mining and Principal component analysis.
Her work in Artificial intelligence addresses subjects such as Calibration, which are connected to disciplines such as Principal component regression.
Her Pattern recognition research is multidisciplinary, incorporating perspectives in Statistics, Multivariate statistics and Outlier.
Her Data mining research is multidisciplinary, relying on both DBSCAN, Cluster analysis and Data set.
Beata Walczak has researched Principal component analysis in several fields, including Environmental chemistry, Visualization and Chemometrics.
Her biological study spans a wide range of topics, including Algorithm, Regression and Robustness.
She most often published in these fields:
- Artificial intelligence (36.32%)
- Pattern recognition (31.84%)
- Chromatography (15.70%)
What were the highlights of her more recent work (between 2012-2021)?
- Chromatography (15.70%)
- Analytical chemistry (10.76%)
- Column (2.69%)
In recent papers she was focusing on the following fields of study:
Beata Walczak mainly investigates Chromatography, Analytical chemistry, Column, Pairwise comparison and Artificial intelligence.
Her biological study deals with issues like Analysis of variance, which deal with fields such as Projection, Component analysis, Image warping, Data set and External Data Representation.
The Chemometrics research she does as part of her general Analytical chemistry study is frequently linked to other disciplines of science, such as Fluorescence spectrometry, therefore creating a link between diverse domains of science.
Beata Walczak combines subjects such as Data analysis and Euclidean distance with her study of Pairwise comparison.
As part of her studies on Artificial intelligence, Beata Walczak frequently links adjacent subjects like Pattern recognition.
Beata Walczak has included themes like RANSAC and Identification in her Pattern recognition study.
Between 2012 and 2021, her most popular works were:
- Particle swarm optimization (PSO). A tutorial (319 citations)
- What can go wrong at the data normalization step for identification of biomarkers (55 citations)
- Chemometrics in analytical chemistry-part I: history, experimental design and data analysis tools. (39 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Her scientific interests lie mostly in Analytical chemistry, Chromatography, Analytical Chemistry, Chemometrics and Normalization.
The concepts of her Analytical chemistry study are interwoven with issues in Selenate and Selenium.
Her Chromatography study frequently draws connections between adjacent fields such as Aspalathus.
Her Analytical Chemistry investigation overlaps with other areas such as Chemical data, Analysis tools and Field.
Her Normalization study combines topics from a wide range of disciplines, such as RANSAC and Pattern recognition.
The study incorporates disciplines such as Sample, Compositional data, Identification, Coda and Prism in addition to Pattern recognition.
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