1 STRING KERNELS WITH FEATURE SELECTION FOR SVM PROTEIN CLASSIFICATION (2009)
We introduce a general framework for string kernels. This framework can produce various types of kernels, including a number of existing kernels, to be used with support vector machines (SVMs). In...
The similarity of two gene products can be used to solve many problems in information biology. Since one gene product corresponds to several GO (Gene Ontology) terms, one way to calculate the gene...
CLASSIFICATION OF PROTEIN SEQUENCES BASED ON WORD SEGMENTATION METHODS (2009)
Yang Yang, Bao-liang Lu, Wen-yun Yang
Protein sequences contain great potential revealing protein function, structure families and evolution information. Classifying protein sequences into different functional groups or families based on...
Computational prediction of novel non-coding RNAs in Arabidopsis thaliana (2009)
Song, Dandan, Yang, Yang, Yu, Bin, Zheng, Binglian, Deng, Zhidong, Lu, Bao-Liang, ...
Abstract Background Non-coding RNA (ncRNA) genes do not encode proteins but produce functional RNA molecules that play crucial roles in many key biological processes. Recent genome-wide...
Computational Prediction of Novel Non-Coding RNAs in Arabidopsis thaliana (2009)
Song, Dandan, Yang, Yang, Yu, Bin, Zheng, Binglian, Deng, Zhidong, Lu, Bao-Liang, ...
Background: Non-coding RNA (ncRNA) genes do not encode proteins but produce functional RNA molecules that play crucial roles in many key biological processes. Recent genome-wide transcriptional...
Gender Classification Using a Min-Max Modular Support Vector Machine with Incorporating Prior (2008)
Hui-cheng Lian, Bao-liang Lu, Senior Member
Abstract — Gender classification based on facial images is a large-scale, complicated two-class classification problem by nature. The reason is that few knowledge is known about the mechanism of...
An Algorithm for Pruning Redundant Modules in Min-Max Modular Network (2008)
Abstract — The min-max modular (M 3) network is a framework that is capable of solving large-scale pattern classification problems in a parallel way. The M 3 network has been successfully applied...
Jonghan Shin, Bao-liang Lu, Arkadi Talnov, Gen Matsumoto, Jurij Brankack
Neurocomputing 38}40 (2001) 1557}1566 Reading auditory discrimination behaviour of freely moving rats from hippocampal EEG
A Confident Majority Voting Strategy for Parallel and Modular Support Vector Machines (2008)
Abstract. Support vector machines (SVMs) has been accepted as a fashionable method in machine learning community, but it cannot be easily scaled to handle large scale problems for its time and space...
Incremental Learning of Support Vector Machines by Classifier Combining (2008)
Abstract. How to acquire new knowledge from new added training data while retaining the knowledge learned before is an important problem for incremental learning. In order to handle this problem, we...
A Modular Reduction Method for k-NN Algorithm with Self-Recombination Learning (2008)
Abstract. A difficulty faced by existing reduction techniques for k-NN algorithm is to require loading the whole training data set. Therefore, these approaches often become inefficient when they are...
Multi-View Face Recognition with Min-Max Modular SVMs (2008)
As a result of statistical learning theory, support vector machines (SVMs)[23] are effective classifiers for the classification problems. SVMs have been successfully applied to various pattern...
Bao-liang Lu, Qing Ma, Michinori Ichikawa, Hitoshi Isahara
Abstract: This paper presents a massively parallel tagging method for automatically assigning the correct part of speech (POS) tag to each ambiguous word in a sentence in the context of the sentence....
Zhao, Hai, Huang, Chang-Ning, Li, Mu, Lu, Bao-Liang
PACLIC 20 / Wuhan, China / 1-3 November, 2006
Efficient classification of multilabel and imbalanced data using min-max modular classifiers (2006)
Abstract — Many real-world applications, such as text categorization and subcellular localization of protein sequences, involve multi-label classification with imbalanced data. In this paper, we...
Multi-view gender classification using local binary patterns and support vector machines (2006)
Abstract. In this paper, we present a novel approach to multi-view gender classification considering both shape and texture information to represent facial image. The face area is divided into small...
L.: Gender Recognition Using a Min-Max Modular Support Vector Machine (2005)
Hui-cheng Lian, Bao-liang Lu, Satoshi Hosoi
Abstract. Considering the fast respond and high generalization accuracy of the min-max modular support vector machine (M 3-SVM), we apply M 3-SVM to solving the gender recognition problem and propose...