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  1. Receptive fields concentrates on the areas of the input space where the input vectors exists and cluster the similar input vector?
  2. How to count integers in vectors inside data frame?
  3. Why do some function operate on vectors within a data frame and others over the whole dataframe?
  4. The hausdorff distance has also been modified to manage these new feature vectors?
  5. Each facial image is divided into five blocks that are further used as feature vectors to a one-class svm classification?
  6. The listing vectors are completely formulated with relation to p0 which ultimately provide an absolute conversion?
  7. What is the minimum number of unequal vectors that can give a vector sum of zero??
  8. Similarly different areas dealing with eigen values and vectors have problems linked to the noise immunity as well as simplicity to reach the result?
  9. The functionality of the microcontrollers is the same but just the small difference is that the register names used are different and various interrupt vectors are generated?
  10. The hausdorff distance has also been modified in order to manage these new feature vectors?