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- W2604472008 abstract "Abstract The paper explores possibility of improving Support Vector Machine-based classification performance by introducing an input data dimensionality reduction step. Feature extraction by means of two different kernel methods are considered: kernel Principal Component Analysis (kPCA) and Supervised kernel Principal Component Analysis. It is hypothesized that input domain transformation, aimed at emphasizing between-class differences, would facilitate classification problem. Experiments, performed on three different datasets show that one can benefit from the proposed approach, as it provides lower variability in classification performance at similar, high recognition rates." @default.
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- W2604472008 date "2016-09-01" @default.
- W2604472008 modified "2023-09-30" @default.
- W2604472008 title "Object Classification Using Support Vector Machines with Kernel-based Data Preprocessing" @default.
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- W2604472008 doi "https://doi.org/10.1515/ipc-2016-0015" @default.
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