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For the Inventor, By the Inventor

Thalwil, Switzerland

André Elisseeff

USPTO Granted Patents = 11 

Average Co-Inventor Count = 3.9

ph-index = 8

Forward Citations = 267(Granted Patents)


Location History:

  • Thawil, CH (2008)
  • Thalwil, CH (2008 - 2013)

Company Filing History:

goldMedal11 out of 36 
 
Health Discovery Corporation
 patents

Years Active: 2008-2013

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11 patents (USPTO):Explore Patents
1. 8489531 - Identification of co-regulation patterns by unsupervised cluster analysis of gene expression data (2013-07-16),
2. 8463718 - Support vector machine-based method for analysis of spectral data (2013-06-11),
3. 7970718 - Method for feature selection and for evaluating features identified as significant for classifying data (2011-06-28),
4. 7890445 - Model selection for cluster data analysis (2011-02-15),
5. 7805388 - Method for feature selection in a support vector machine using feature ranking (2010-09-28),
6. 7788193 - Kernels and methods for selecting kernels for use in learning machines (2010-08-31),
7. 7676442 - Selection of features predictive of biological conditions using protein mass spectrographic data (2010-03-09),
8. 7617163 - Kernels and kernel methods for spectral data (2009-11-10),
9. 7475048 - Pre-processed feature ranking for a support vector machine (2009-01-06),
10. 7353215 - Kernels and methods for selecting kernels for use in learning machines (2008-04-01),
11. 7318051 - Methods for feature selection in a learning machine (2008-01-08),

Title: André Elisseeff: Innovator in Data Analysis and Machine Learning

Introduction

André Elisseeff is a prominent inventor based in Thalwil, Switzerland. He has made significant contributions to the fields of data analysis and machine learning, holding a total of eight patents. His work focuses on improving algorithms for clustering and feature selection, which are essential in various applications of artificial intelligence.

Latest Patents

One of his latest patents is titled "Model selection for cluster data analysis." This patent provides a method for selecting the number of clusters in a clustering algorithm by comparing the similarity between pairs of clustering runs on sub-samples of data. The algorithm identifies stable patterns in the data, making it applicable to any clustering method. Another notable patent is "Method for feature selection in a support vector machine using feature ranking." This invention outlines a pre-processing step that reduces the number of features processed by a learning machine, enhancing the efficiency of pattern classification, regression, clustering, and novelty detection.

Career Highlights

André currently works at Health Discovery Corporation, where he applies his expertise in data analysis and machine learning. His innovative approaches have contributed to advancements in the field, making significant impacts on how data is interpreted and utilized.

Collaborations

Throughout his career, André has collaborated with notable professionals such as Jason Aaron, Edward Weston, and Bernhard Schoelkopf. These collaborations have fostered a rich exchange of ideas and have further propelled his research and inventions.

Conclusion

André Elisseeff's work exemplifies the intersection of innovation and technology in data analysis and machine learning. His patents reflect a commitment to enhancing algorithmic efficiency and effectiveness, contributing to the advancement of the field.

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