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Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs
March 1981 (vol. 3 no. 3)
pp. 293-298
| ASCII Text | x | ||
| G. R. Dattatreya, V. V. S. Sarma, "Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3, no. 3, pp. 293-298, March, 1981. | |||
| BibTex | x | ||
| @article{ 10.1109/TPAMI.1981.4767102, author = {G. R. Dattatreya and V. V. S. Sarma}, title = {Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {3}, number = {3}, issn = {0162-8828}, year = {1981}, pages = {293-298}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.1981.4767102}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs IS - 3 SN - 0162-8828 SP293 EP298 EPD - 293-298 A1 - G. R. Dattatreya, A1 - V. V. S. Sarma, PY - 1981 VL - 3 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
The minimum cost classifier when general cost functions are associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimization of the binary tree in this context is carried out using dynamic programming. This technique is applied to the voiced-unvoiced-silence classification in speech processing.
Citation:
G. R. Dattatreya, V. V. S. Sarma, "Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3, no. 3, pp. 293-298, March 1981, doi:10.1109/TPAMI.1981.4767102
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