Max-Margin Infinite Hidden Markov Models
Authors: Aonan Zhang, Jun Zhu, Bo Zhang
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on synthetic and real data sets show that our methods obtain superior performance than other competitors in both single variate classification and sequential prediction tasks. |
| Researcher Affiliation | Academia | Aonan Zhang ZAN12@TSINGHUA.EDU.CN Jun Zhu DCSZJ@TSINGHUA.EDU.CN Bo Zhang DCSZB@TSINGHUA.EDU.CN Dept. of Comp. Sci. & Tech., TNList Lab, State Key Lab of Intell. Tech. & Sys., Tsinghua University, China |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the methodology described is publicly available. |
| Open Datasets | Yes | Parkinsons data: 'We extract 10 principal components using PCA, following (Shahbaba & Neal, 2009).'; Protein data: 'We follow (Ding & Dubchak, 2001) to split the data set into a training set...'; RGBD-Hu Da Act: 'RGBD-Hu Da Act is a home-monitoring human activity recognition data set containing both color and depth video streams (Ni et al., 2011).' |
| Dataset Splits | Yes | We adopt 5-fold cross-validation and report the average performance as well as standard deviations. (for Parkinsons and RGBD-Hu Da Act); We follow (Ding & Dubchak, 2001) to split the data set into a training set containing 313 instances and a test set consisting of 385 instances. (for Protein data) |
| Hardware Specification | Yes | All the experiments were conducted on an Intel Core i5 3.10GHZ computer with 4.0GB RAM. |
| Software Dependencies | No | The paper only states the programming language used: 'We implemented our models and re-implemented DPMNL and i M2EDM using C++.' No specific software library or dependency versions are provided. |
| Experiment Setup | Yes | In this experiment we set the initial number of states K0 = 10, the HDP concentration hyper-parameters α0 = 2, γ0 = 2, and the largemargin classifier hyper-parameters c=1, ℓ=1.6. For models based on Gibbs classifiers we set K0=20 and run 300 iterations. While for i M2EDM we set the truncation level K=20 and run 100 iterations for training. |