Ensemble Methods for Structured Prediction
Authors: Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri
ICML 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also report the results of extensive experiments with these algorithms in several structured prediction tasks. |
| Researcher Affiliation | Collaboration | Corinna Cortes EMAIL Google Research, 111 8th Avenue, New York, NY 10011 Vitaly Kuznetsov EMAIL Courant Institute of Mathematical Sciences, 251 Mercer Street, New York, NY 10012 Mehryar Mohri EMAIL Courant Institute and Google Research, 251 Mercer Street, New York, NY 10012 |
| Pseudocode | Yes | Algorithm 1 WMWP algorithm. Inputs: sample {(x1, y1), . . . , (x T , y T )}; set of experts {h1, . . . , hp}; parameter β (0, 1). for j = 1 to p and k = 1 to l do... Algorithm 2 ESPBoost Algorithm. Inputs: S = ((x1, y1), . . . , (xm, ym)); set of experts {h1, . . . , hp}. for i = 1 to m and k = 1 to l do... |
| Open Source Code | No | The paper mentions and links to several third-party software packages used (e.g., CRFsuite, SVMstruct, Stanford Classifier), but it does not state that the authors' own code for the described methodology is open-source or provide a link to it. |
| Open Datasets | Yes | Rob Kassel s OCR data set is available for download from http://ai.stanford.edu/ btaskar/ocr/.; The Penn Treebank 2 data set is available through LDC license at http://www.cis.upenn.edu/ treebank/ and contains 251,854 sentences with a total of 6,080,493 tokens and 45 different parts of speech. |
| Dataset Splits | Yes | For each data set, we performed 10-fold cross-validation with disjoint training sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions several software packages used (e.g., 'CRFsuite', 'SVMstruct', 'Stanford Classifier') with citations, but it does not specify their version numbers. |
| Experiment Setup | Yes | More details on the data set and the experimental parameters can be found in Appendix H.1. Table 1. Average Normalized Hamming Loss, ADS1 and ADS2. βADS1 = 0.95, βADS2 = 0.95, TSLE = 100, δ = 0.05. Table 2. Average Normalized Hamming Loss, PDS1 and PDS2. βPDS1 = 0.85, βPDS2 = 0.97, TSLE = 100, δ = 0.05. Table 3. Average Normalized Hamming Loss, TR1 and TR2. βTR1 = 0.95, βTR2 = 0.98, TSLE = 100, δ = 0.05. |