Self-Bounded Prediction Suffix Tree via Approximate String Matching
Authors: Dongwoo Kim, Christian Walder
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on synthetic datasets as well as three real-world datasets, we show that the approximate matching PST results in better predictive performance than the other variants of PST. In Section 5 and 6, we verify our approach on synthetic datasets and demonstrate the improved predictive performance of our model on three real-world datasets. |
| Researcher Affiliation | Collaboration | 1Australian National University, Canberra, ACT, Australia 2Data to Decisions CRC, Kent Town, SA, Australia 3Data61 at CSIRO, Canberra, ACT, Australia. |
| Pseudocode | Yes | Algorithm 1 Online learning algorithm for unbounded a PST. and Algorithm 2 Online learning algorithm for self-bounded a PST. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use three datasets: a symbolic music dataset (Walder, 2016) from which we retain midi onset events only, a system call dataset (Hofmeyr et al., 1998), and human activity dataset (Ord onez et al., 2013). |
| Dataset Splits | Yes | For every experiment, we use the first 40% of a sequence to train, the subsequent 20% of the sequence to validate, and the final 40% of sequence to test the models. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For both parameter λ and ϵ, we test all possible configuration of λ = {2, 4, 6, 8, 10, 12}, ξ = (0.5, 0.7, 0.9, 0.99), and ϵ = {0, 1} and choose the best model based on the accuracy of validation set. |