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个人简介
My Projects:
Pattern Classification and Machine Learning:
Optimum Margin Classifiers and Support Vector Machines: A new technique of classification, which leaves the largest possible margin on either side of the decision boundary, therefore reducing classification errors. Our invention was granted a US patent. Take a look at the impressive SVM application list. For further information, consult the kernel machines web site, the support vector machines web site, and the list of available SVM software.
Optimum test set size: What is the minimum number of samples needed to estimate the performance of your classifier with a certain accuracy? Lambert Schomaker wrote an Applet based on the results of our research.
Variable and feature selection: Methods for selecting just a few variables relevant for classification, with applications to gene selection, drug discovery, text mining, and others. See the special issue of JMLR we co-edited and the benchmark on feature extraction we organized. See also the book we edited and the class we taught.
Biometrics, Genomics, Proteomics and Cancer research:
Fingerprint verification: Your fingerprint may be stored on your credit card in the future. We devised a method to represent fingerprints in a compact way using directional and frequency maps.
Writer identification: Handwriting has always been used by forensic experts for law enforcement purpose. Fighting against terrorism has recently incrased the needs for accurate writer indentification. We contributed to the design of the Wanda XML format, a new standard to store and annotate handwriting pieces of evidence.
Gene selection: A project to discover genes that may be connected to cancer or other diseases by analyzing patterns of gene expression obtained from DNA microarrays. A patent is pending on our invention of RFE-SVM (Recursive Feature Elimination SVM).
Diagnosis from protein profiles: Gene activity monitors only indirectly protein regulation. Disease states can be better assessed by profiling protein amounts directly. We use antibody array and mass-spectrometry data to help diagnosing disease, including cancer.
Handwriting Recognition and Pen Computing:
UNIPEN: A project of data exchange and benchmark for on-line handwritten data.
On-line Handwriting Recognizer: A program that can recognize handprinted and cursive words using Time Delay Neural Networks (TDNN) and Hidden Markov Models (HMM). A US patent was granted to our application of TDNNs.
Cursive handwriting teacher: A program which measures how well formed your cursive handwriting is.
Handwriting Synthesizer: A program that synthesizes text with your own handwriting, given an ASCII file.
Language model: A program which learns the statistics of English using Variable Memory Length Markov Models (VLMM) and Weighted Finite State Transductions (WFST). Such models are used, in particular, as handwriting recognition postprocessors.
Wanda: A framework for writer identification with forensic applications.
Pattern Classification and Machine Learning:
Optimum Margin Classifiers and Support Vector Machines: A new technique of classification, which leaves the largest possible margin on either side of the decision boundary, therefore reducing classification errors. Our invention was granted a US patent. Take a look at the impressive SVM application list. For further information, consult the kernel machines web site, the support vector machines web site, and the list of available SVM software.
Optimum test set size: What is the minimum number of samples needed to estimate the performance of your classifier with a certain accuracy? Lambert Schomaker wrote an Applet based on the results of our research.
Variable and feature selection: Methods for selecting just a few variables relevant for classification, with applications to gene selection, drug discovery, text mining, and others. See the special issue of JMLR we co-edited and the benchmark on feature extraction we organized. See also the book we edited and the class we taught.
Biometrics, Genomics, Proteomics and Cancer research:
Fingerprint verification: Your fingerprint may be stored on your credit card in the future. We devised a method to represent fingerprints in a compact way using directional and frequency maps.
Writer identification: Handwriting has always been used by forensic experts for law enforcement purpose. Fighting against terrorism has recently incrased the needs for accurate writer indentification. We contributed to the design of the Wanda XML format, a new standard to store and annotate handwriting pieces of evidence.
Gene selection: A project to discover genes that may be connected to cancer or other diseases by analyzing patterns of gene expression obtained from DNA microarrays. A patent is pending on our invention of RFE-SVM (Recursive Feature Elimination SVM).
Diagnosis from protein profiles: Gene activity monitors only indirectly protein regulation. Disease states can be better assessed by profiling protein amounts directly. We use antibody array and mass-spectrometry data to help diagnosing disease, including cancer.
Handwriting Recognition and Pen Computing:
UNIPEN: A project of data exchange and benchmark for on-line handwritten data.
On-line Handwriting Recognizer: A program that can recognize handprinted and cursive words using Time Delay Neural Networks (TDNN) and Hidden Markov Models (HMM). A US patent was granted to our application of TDNNs.
Cursive handwriting teacher: A program which measures how well formed your cursive handwriting is.
Handwriting Synthesizer: A program that synthesizes text with your own handwriting, given an ASCII file.
Language model: A program which learns the statistics of English using Variable Memory Length Markov Models (VLMM) and Weighted Finite State Transductions (WFST). Such models are used, in particular, as handwriting recognition postprocessors.
Wanda: A framework for writer identification with forensic applications.
研究兴趣
论文共 313 篇作者统计合作学者相似作者
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CoRR (2024)
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Haocheng Yuan,Ajian Liu, Junze Zheng,Jun Wan,Jiankang Deng,Sergio Escalera,Hugo Jair Escalante,Isabelle Guyon,Zhen Lei
arxiv(2024)
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Luis Oala,Manil Maskey, Lilith Bat-Leah,Alicia Parrish,Nezihe Merve Gürel,Tzu-Sheng Kuo,Yang Liu,Rotem Dror, Danilo Brajovic,Xiaozhe Yao,Max Bartolo,William Gaviria Rojas,
CoRR (2023)
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Adrien Pavao,Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran,Xavier Baró,Hugo Jair Escalante,Sergio Escalera, Tyler Thomas,Zhen Xu
J. Mach. Learn. Res. (2023): 198:1-198:6
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Comput. Softw. Big Sci.no. 1 (2023): 1-19
2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN (2023): 1-9
2023 IEEE 19th International Conference on e-Science (e-Science)pp.1-10, (2023)
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