Pattern Recognition and Machine Learning
Citations
225 citations
Cites background from "Pattern Recognition and Machine Lea..."
...This discovery led to the foundation of artificial neural networks (ANNs) [2] and subsequently deep learning [3]....
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224 citations
Cites background from "Pattern Recognition and Machine Lea..."
...More details can be found elsewhere (Bishop, 2006; Schölkopf and Smola, 2002; Tipping, 2000)....
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...Additionally, inter-regional dependencies are taken into account (Bishop, 2006; Schölkopf and Smola, 2002), such as the widespread microstructural changes inWM, which were recently found to be associated with corresponding age-related changes in cortical GM regions in adolescents (Giorgio et al....
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...Additionally, inter-regional dependencies are taken into account (Bishop, 2006; Schölkopf and Smola, 2002), such as the widespread microstructural changes inWM, which were recently found to be associated with corresponding age-related changes in cortical GM regions in adolescents (Giorgio et al.,…...
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224 citations
Cites background from "Pattern Recognition and Machine Lea..."
...In addition, it can lead to inconsistent results in which 298 observations are assigned to multiple classes simultaneously (Bishop, 2006)....
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...Instead of making strict assumptions about the data, machine learning models50 learn to represent complex relationships in a data-driven manner (e.g. Bishop, 2006)....
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...In addition, it can lead to inconsistent results in which298 observations are assigned to multiple classes simultaneously (Bishop, 2006)....
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224 citations
Additional excerpts
...solution for ordinary least squares [38], which is given by...
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224 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...…observation states p(Xt|Ht); to verify that the temporal incorporation in our model is a more effective approach for action recognition against the Bag-of-Visual-Word approach, we compare against the EigenJoint-Naive Bayes Nearest Neighbour [30] where the same set of raw features have been used....
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...Currently the model parameters are predominantly learnt by Gaussian mixture models using expectation maximization [1, 21]....
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