RV model-based kernel with LSM
This repository contains the source code for classification based on the liquid state machine (LSM). The paper is published in ECML-16. You can find electronic version of paper in here.
We build our project on the basis of codes provided by Fengzhen Tang (firstname.lastname@example.org). This repository integrates work from other literatures. All the contact information is retained in the preamble. ALL necessary codes can be obtained freely from the Internet.
demo.m is a script to allow a quick view, and from which a quick start for your own project.
How to start?
- download the data sets from website here (The data sets are not included in this repo.)
- firstly, run
initpathto include some necessary folders.
SamplingRVto test classification.
- we have included a data set for testing purpose. Run
demo.m, you will see the experimental result on that data.
How to adjust reservoir size (R_no) and regression coefficient (val) to get a best fitting?
- You may run
lsm_weight_*.mdirectly to see the fitting error, and you may also integrate your own own algorithm in adjusting
test/test.mis a useful in finding best combination of
- In addition, default settings in
test/test.mshould not be ignored and they are good starting points for a beginner.
- You may run
How to adjust svm parameters in svm, i.e.
SamplingRVby feeding a list of
kp, the best classification accuracy will come out.
This repository includes a precompiled version of
csim 1.1.1 and
libsvm 3.2, including mexw64, mexa64m, mexmaci64 for usage under Windows, Linux, Mac.
The csim can be found here. N.B. the original version from the website was altered to meet the updated operational environment.
libsvm 3.2 or higher version is required. libsvm is a library for support vector machines which was obtained from here.
This repository is mainly contributed by Junyuan Hong(email@example.com), in cooperation with Yang Li.
This repository is distributed under the GNU General Public License.
Please cite our paper as follow, if you use our codes.
Li, Y., Hong, J., & Chen, H. (2016, September). Sequential Data Classification in the Space of Liquid State Machines. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 313-328). Springer International Publishing.