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![superpositions can be achieved reliably by using an iterative algorithm based on template matching [37]. It is possible that basis functions other than principal components can provide successful classification. A promising candidate that we are investigating, the wavelet transform [40], is especially suited for an efficient representation of transient signals. Since its orthonormal basis of the data covariance matrix and its eigenvectors. of neural networks to of patterns are uncorrelated (whitened data), the of each class is spherical around its centroid. In such cases, the of neural networks does not provide an advantage of appropriate stimulus for a given recording site. On-line and fully is becoming](https://images.tandf.co.uk/common/jackets/crclarge/978113898/9781138985384.jpg "superpositions can be achieved reliably by using an iterative algorithm based on template matching [37]. It is possible that basis functions other than principal components can provide successful classification. A promising candidate that we are investigating, the wavelet transform [40], is especially suited for an efficient representation of transient signals. Since its orthonormal basis of the data covariance matrix and its eigenvectors. of neural networks to of patterns are uncorrelated (whitened data), the of each class is spherical around its centroid. In such cases, the of neural networks does not provide an advantage of appropriate stimulus for a given recording site. On-line and fully is becoming")
Chapter
superpositions can be achieved reliably by using an iterative algorithm based on template matching [37]. It is possible that basis functions other than principal components can provide successful classification. A promising candidate that we are investigating, the wavelet transform [40], is especially suited for an efficient representation of transient signals. Since its orthonormal basis of the data covariance matrix and its eigenvectors. of neural networks to of patterns are uncorrelated (whitened data), the of each class is spherical around its centroid. In such cases, the of neural networks does not provide an advantage of appropriate stimulus for a given recording site. On-line and fully is becoming
DOI link for superpositions can be achieved reliably by using an iterative algorithm based on template matching [37]. It is possible that basis functions other than principal components can provide successful classification. A promising candidate that we are investigating, the wavelet transform [40], is especially suited for an efficient representation of transient signals. Since its orthonormal basis of the data covariance matrix and its eigenvectors. of neural networks to of patterns are uncorrelated (whitened data), the of each class is spherical around its centroid. In such cases, the of neural networks does not provide an advantage of appropriate stimulus for a given recording site. On-line and fully is becoming
superpositions can be achieved reliably by using an iterative algorithm based on template matching [37]. It is possible that basis functions other than principal components can provide successful classification. A promising candidate that we are investigating, the wavelet transform [40], is especially suited for an efficient representation of transient signals. Since its orthonormal basis of the data covariance matrix and its eigenvectors. of neural networks to of patterns are uncorrelated (whitened data), the of each class is spherical around its centroid. In such cases, the of neural networks does not provide an advantage of appropriate stimulus for a given recording site. On-line and fully is becoming
ABSTRACT
superpositions can be achieved reliably by using an iterative algorithm
