A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Data-Dependent Kernel Machines for Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A theory of learning with similarity functions
Machine Learning
Efficient multiclass maximum margin clustering
Proceedings of the 25th international conference on Machine learning
An efficient non-dominated sorting method for evolutionary algorithms
Evolutionary Computation
Theory and algorithm for learning with dissimilarity functions
Neural Computation
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
KNN-kernel density-based clustering for high-dimensional multivariate data
Computational Statistics & Data Analysis
WEKA---Experiences with a Java Open-Source Project
The Journal of Machine Learning Research
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Chronic neck pain is a common morbid disorder in modern society. Acupuncture has been administered for treating chronic pain as an alternative therapy for a long time, with its effectiveness supported by the latest clinical evidence. However, the potential effective difference in different syndrome types is questioned due to the limits of sample size and statistical methods. We applied machine learning methods in an attempt to solve this problem. Through a multi-objective sorting of subjective measurements, outstanding samples are selected to form the base of our kernel-oriented model. With calculation of similarities between the concerned sample and base samples, we are able to make full use of information contained in the known samples, which is especially effective in the case of a small sample set. To tackle the parameters selection problem in similarity learning, we propose an ensemble version of slightly different parameter setting to obtain stronger learning. The experimental result on a real data set shows that compared to some previous well-known methods, the proposed algorithm is capable of discovering the underlying difference among different syndrome types and is feasible for predicting the effective tendency in clinical trials of large samples.