What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Self-organizing map, Artificial intelligence, Information retrieval, Data mining and Machine learning.
He combines subjects such as Exploratory data analysis, Similarity, Data visualization and Metric with his study of Self-organizing map.
His Artificial intelligence research focuses on subjects like Pattern recognition, which are linked to Cluster analysis.
His work carried out in the field of Information retrieval brings together such families of science as Difference-map algorithm, Word, Histogram and The Internet, World Wide Web.
His Data mining research focuses on Data set and how it relates to Measure.
His Machine learning study incorporates themes from Probabilistic logic, Inference, Bayesian probability and Canonical correlation.
His most cited work include:
- Self organization of a massive document collection (845 citations)
- WEBSOM - Self-Organizing Maps of Document Collections (460 citations)
- Dimensionality reduction by random mapping: fast similarity computation for clustering (352 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Machine learning, Data mining, Pattern recognition and Bayesian probability.
His Artificial intelligence study frequently draws connections between adjacent fields such as Relevance.
His work deals with themes such as Multi-task learning and Inference, which intersect with Machine learning.
As part of one scientific family, Samuel Kaski deals mainly with the area of Data mining, narrowing it down to issues related to the Bayesian inference, and often Multivariate statistics.
His Bayesian probability research incorporates elements of Matrix decomposition, Algorithm and Canonical correlation.
His research brings together the fields of Information visualization and Self-organizing map.
He most often published in these fields:
- Artificial intelligence (47.44%)
- Machine learning (26.98%)
- Data mining (18.34%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (47.44%)
- Machine learning (26.98%)
- Bayesian probability (17.11%)
In recent papers he was focusing on the following fields of study:
Samuel Kaski focuses on Artificial intelligence, Machine learning, Bayesian probability, Algorithm and Inference.
His Artificial intelligence study frequently intersects with other fields, such as Pattern recognition.
In his research, Contrast and Linear regression is intimately related to Human-in-the-loop, which falls under the overarching field of Machine learning.
Samuel Kaski interconnects Structure and Latent variable in the investigation of issues within Bayesian probability.
His studies in Algorithm integrate themes in fields like Matrix decomposition, Scalability and Bayesian optimization.
He has included themes like Mixture model, Probabilistic logic, Set and Statistical model in his Inference study.
Between 2018 and 2021, his most popular works were:
- Plasmids shaped the recent emergence of the major nosocomial pathogen Enterococcus faecium (27 citations)
- Deep learning with differential Gaussian process flows (21 citations)
- Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches. (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Samuel Kaski mainly focuses on Artificial intelligence, Bayesian probability, Inference, Algorithm and Approximate Bayesian computation.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Scale parameter, Machine learning and Pattern recognition.
His study in Bayesian probability is interdisciplinary in nature, drawing from both Brain activity and meditation, Flux balance analysis, Monte Carlo method and Neuroscience.
His research in Inference intersects with topics in Bayesian optimization, Robust statistics, Statistical inference, Probabilistic classification and Likelihood function.
In Algorithm, Samuel Kaski works on issues like Matrix decomposition, which are connected to Embarrassingly parallel, Markov chain Monte Carlo, Missing data and Biological data.
His Approximate Bayesian computation research includes themes of Probability distribution, Outbreak, Bayesian statistics, Computational model and Computational statistics.
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