K. P. Soman - IEEE Xplore Author Profile
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K. P. Soman

Also published under: Soman K. P., Kp Soman

Publications
162
Citations
4,177
Publications by Year
20032024
Co-Authors:
Show All Co-Authors (262)
Image of author K. P. Soman

K. P. Soman

Also published under: Soman K. P., Kp Soman

Affiliation

Amrita School of Artificial Intelligence, Coimbatore
Vignan’s Foundation for Science, Technology Research university, Guntur

Publication Topics

Biography

K. P. Soman is currently the Head and a Professor with the Center for Computational Engineering and Networking (CEN) and the Dean of the Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India. He has more than 25 years of research and teaching experience in artificial intelligence and data science related subjects with the Amrita School of Engineering, Coimbatore. He has around 450 publications to his credit in reputed journals, such as IEEE Transactions, IEEE Access, Applied Energy, and conference proceedings. He published four books, namely, Insight Into Wavelets, Insight Into Data Mining, Support Vector Machines and Other Kernel Methods, and Signal and Image Processing-The Sparse Way. He is the most cited...
Publications
162
Citations
4,177
Publications by Year
20032024
Co-Authors:
Show All Co-Authors (262)
Author's Published Works

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Multidimensional splines with infinite number of knots as SVM kernels

Rauf Izmailov;Vladimir Vapnik;Akshay Vashist

The 2013 International Joint Conference on Neural Networks (IJCNN)
Year: 2013 | Conference Paper |
Cited by: Papers (12)
Radial basis function (RBF) kernels for SVM have been routinely used in a wide range of classification problems, delivering consistently good performance for those problems where the kernel computations are numerically feasible (high-dimensional problems typically use linear kernels). One of the drawbacks of RBF kernels is the necessity of selecting the proper value of the hyperparameter γ in addi...Show More

Multidimensional splines with infinite number of knots as SVM kernels

Rauf Izmailov;Vladimir Vapnik;Akshay Vashist

Year: 2013 | Conference Paper |

Learning using hidden information (Learning with teacher)

Vladimir Vapnik;Akshay Vashist;Natalya Pavlovitch

2009 International Joint Conference on Neural Networks
Year: 2009 | Conference Paper |
Cited by: Papers (25)
In this paper we consider a new paradigm of learning: learning using hidden information. The classical paradigm of the supervised learning is to learn a decision rule from labeled data (xi, yi), xi isin X, yi isin {-1, 1}, i = 1, hellip, lscr. In this paper we consider a new setting: given training vectors in space X along with labels and description of this data in another space X*, find in space...Show More

Learning using hidden information (Learning with teacher)

Vladimir Vapnik;Akshay Vashist;Natalya Pavlovitch

Year: 2009 | Conference Paper |

A support vector clustering method

A. Ben-Hur;D. Horn;H.T. Siegelmann;V. Vapnik

Proceedings 15th International Conference on Pattern Recognition. ICPR-2000
Year: 2000 | Conference Paper |
Cited by: Papers (177)
We present a novel kernel method for data clustering using a description of the data by support vectors. The kernel reflects a projection of the data points from data space to a high dimensional feature space. Cluster boundaries are defined as spheres in feature space, which represent complex geometric shapes in data space. We utilize this geometric representation of the data to construct a simple...Show More

A support vector clustering method

A. Ben-Hur;D. Horn;H.T. Siegelmann;V. Vapnik

Year: 2000 | Volume: 2 | Conference Paper |

SVM method of estimating density, conditional probability, and conditional density

V. Vapnik

2000 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2000 | Conference Paper |
Cited by: Papers (17)
The problem of estimating density, conditional probability, and conditional density is considered as an ill-posed problem of solving integral equations. To solve these equations the support vector method (SVM) is used.Show More

SVM method of estimating density, conditional probability, and conditional density

V. Vapnik

Year: 2000 | Volume: 2 | Conference Paper |

Support vector machines for spam categorization

H. Drucker;Donghui Wu;V.N. Vapnik

IEEE Transactions on Neural Networks
Year: 1999 | Volume: 10, Issue: 5 | Journal Article |
We study the use of support vector machines (SVM) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 700...Show More

Support vector machines for spam categorization

H. Drucker;Donghui Wu;V.N. Vapnik

Year: 1999 | Volume: 10, Issue: 5 | Journal Article |

An overview of statistical learning theory

V.N. Vapnik

IEEE Transactions on Neural Networks
Year: 1999 | Volume: 10, Issue: 5 | Journal Article |
Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990's new types of learning algorithms (called support vector machines) based on the developed theory were proposed. This made statistical learning theory not only a tool for the theoretical...Show More

An overview of statistical learning theory

V.N. Vapnik

Year: 1999 | Volume: 10, Issue: 5 | Journal Article |

Model complexity control for regression using VC generalization bounds

V. Cherkassky;Xuhui Shao;F.M. Mulier;V.N. Vapnik

IEEE Transactions on Neural Networks
Year: 1999 | Volume: 10, Issue: 5 | Journal Article |
Cited by: Papers (146)
It is well known that for a given sample size there exists a model of optimal complexity corresponding to the smallest prediction (generalization) error. Hence, any method for learning from finite samples needs to have some provisions for complexity control. Existing implementations of complexity control include penalization (or regularization), weight decay (in neural networks), and various greed...Show More

Model complexity control for regression using VC generalization bounds

V. Cherkassky;Xuhui Shao;F.M. Mulier;V.N. Vapnik

Year: 1999 | Volume: 10, Issue: 5 | Journal Article |

Support vector machines for histogram-based image classification

O. Chapelle;P. Haffner;V.N. Vapnik

IEEE Transactions on Neural Networks
Year: 1999 | Volume: 10, Issue: 5 | Journal Article |
Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x, y)=e/sup -/spl rho///spl Sigma//sub i//sup...Show More

Support vector machines for histogram-based image classification

O. Chapelle;P. Haffner;V.N. Vapnik

Year: 1999 | Volume: 10, Issue: 5 | Journal Article |

What size test set gives good error rate estimates?

I. Guyon;J. Makhoul;R. Schwartz;V. Vapnik

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 1998 | Volume: 20, Issue: 1 | Journal Article |
We address the problem of determining what size test set guarantees statistically significant results in a character recognition task, as a function of the expected error rate. We provide a statistical analysis showing that if, for example, the expected character error rate is around 1 percent, then, with a test set of at least 10,000 statistically independent handwritten characters (which could b...Show More

What size test set gives good error rate estimates?

I. Guyon;J. Makhoul;R. Schwartz;V. Vapnik

Year: 1998 | Volume: 20, Issue: 1 | Journal Article |

Comparing support vector machines with Gaussian kernels to radial basis function classifiers

B. Scholkopf;Kah-Kay Sung;C.J.C. Burges;F. Girosi;P. Niyogi;T. Poggio;V. Vapnik

IEEE Transactions on Signal Processing
Year: 1997 | Volume: 45, Issue: 11 | Journal Article |
The support vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights, and threshold that minimize an upper bound on the expected test error. The present study is devoted t...Show More

Comparing support vector machines with Gaussian kernels to radial basis function classifiers

B. Scholkopf;Kah-Kay Sung;C.J.C. Burges;F. Girosi;P. Niyogi;T. Poggio;V. Vapnik

Year: 1997 | Volume: 45, Issue: 11 | Journal Article |

Comparison of classifier methods: a case study in handwritten digit recognition

L. Bottou;C. Cortes;J.S. Denker;H. Drucker;I. Guyon;L.D. Jackel;Y. LeCun;U.A. Muller;E. Sackinger;P. Simard;V. Vapnik

Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5)
Year: 1994 | Conference Paper |
This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, and memory requirements. When available, we report measurements of the fraction of patterns that must be rejected so that the remaining patterns have misclassification rates less than a given threshold.Show More

Comparison of classifier methods: a case study in handwritten digit recognition

L. Bottou;C. Cortes;J.S. Denker;H. Drucker;I. Guyon;L.D. Jackel;Y. LeCun;U.A. Muller;E. Sackinger;P. Simard;V. Vapnik

Year: 1994 | Volume: 2 | Conference Paper |

Writer-adaptation for on-line handwritten character recognition

N. Matic;I. Guyon;J. Denker;V. Vapnik

Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93)
Year: 1993 | Conference Paper |
The authors have designed a writer-adaptable character recognition system for online characters entered on a touch terminal. It is based on a Time Delay Neural Network (TDNN) that is pre-trained on examples from many writers to recognize digits and uppercase letters. The TDNN without its last layer serves as a preprocessor for an optimal hyperplane classifier that can be easily retrained to peculi...Show More

Writer-adaptation for on-line handwritten character recognition

N. Matic;I. Guyon;J. Denker;V. Vapnik

Year: 1993 | Conference Paper |

Computer aided cleaning of large databases for character recognition

N. Matic;I. Guyon;L. Bottou;J. Denker;V. Vapnik

Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems
Year: 1992 | Conference Paper |
Cited by: Papers (16)
A method for computer-aided cleaning of undesirable patterns in large training databases has been developed. The method uses the trainable classifier itself, to point out patterns that are suspicious, and should be checked by the human supervisor. While suspicious patterns that are meaningless or mislabeled are considered garbage, and removed from the database, the remaining patterns, like ambiguo...Show More

Computer aided cleaning of large databases for character recognition

N. Matic;I. Guyon;L. Bottou;J. Denker;V. Vapnik

Year: 1992 | Conference Paper |

Capacity control in linear classifiers for pattern recognition

I. Guyon;V. Vapnik;B. Boser;L. Bottou;S.A. Solla

Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems
Year: 1992 | Conference Paper |
Cited by: Papers (7) | Patents (3)
Achieving good performance in statistical pattern recognition requires matching the capacity of the classifier to the amount of training data. If the classifier has too many adjustable parameters (large capacity), it is likely to learn the training data without difficulty, but will probably not generalize properly to patterns that do not belong to the training set. Conversely, if the capacity of t...Show More

Capacity control in linear classifiers for pattern recognition

I. Guyon;V. Vapnik;B. Boser;L. Bottou;S.A. Solla

Year: 1992 | Conference Paper |

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