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- W2592429817 abstract "This chapter builds on the results of Chapter 8, Dimensionality Reduction. Now that we managed to represent the original sata in a low-dimensional space, we want to know whether we can identify distinct classes of neurons in this space. We apply methods that we introduced in Chapter 7, Regression (like logistic regression) to do so, but also the popular classification methods of support vector machines and random forests. We further use k -means clustering and address whether these methods are statistically reliable and if they allow us to gain meaningful insights about the neural sataset in question, e.g., whether the sample mean of spiking is different for different classes of neurons. We do so by introducing resampling methods like the bootstrap." @default.
- W2592429817 created "2017-03-16" @default.
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- W2592429817 date "2017-01-01" @default.
- W2592429817 modified "2023-09-25" @default.
- W2592429817 title "Classification and Clustering" @default.
- W2592429817 doi "https://doi.org/10.1016/b978-0-12-804043-0.00009-x" @default.
- W2592429817 hasPublicationYear "2017" @default.
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