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Topic Linux System Programming

by: Kylee VonRueden

Topic Linux System Programming CS 631O

Marketplace > Pace University - New York > ComputerScienence > CS 631O > Topic Linux System Programming
Kylee VonRueden

GPA 3.55


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This 7 page Class Notes was uploaded by Kylee VonRueden on Wednesday September 30, 2015. The Class Notes belongs to CS 631O at Pace University - New York taught by Staff in Fall. Since its upload, it has received 24 views. For similar materials see /class/217108/cs-631o-pace-university-new-york in ComputerScienence at Pace University - New York.

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Date Created: 09/30/15
salmon sea bass I count I I I FIGURE 12 Histograms for the length feature for the two categories No single thresh7 old value of the length will serve to unambiguously discriminate between the two cat egories using length alone we will have some errors The value marked Iquot will lead to the smallest number of errors on average From Richard 0 Duda Peter E Hart and David G Stork Pattern Classification Copyright 2001 by John Wiley amp Sons Inc 14 salmon sea bass lightness 2 4 x 6 8 FIGURE 13 Histograms for the lightness feature for the two categories No single threshold value Xquot decision boundary will serve to unambiguously discriminate be tween the two categories using lightness alone we will have some errors The value Xquot marked will lead to the smallest number of errors on avera et From Richard 0 Duda Peter E Hart and David G Stork Pattern Classification Copyright 2001 by John Wiley amp Sons Inc 22 salmon sea bass 2 20 39 19 39 39 o I 18 39 17 I I6 15 4 lightness 2 4 6 8 FIGURE 14 The two features of lightness and width for sea bass and salmon The dark line could serve as a decision boundary of our classifiert Overall classification error on the data shown is lower than if we use only one feature as in Fig 113 but there will still be some errors From Richard 01 Duda Peter E Hart and David G Stork Pattern Classification Copyright 2001 byJohn Wiley 81 Sons Inc lzghmess 1 0 FIGURE 15 Overly complex models for the fish will lead to decision boundaries that are complicated While such a decision may lead to perfect classification of our training samples it would lead to poor performance on future patterns The novel test point marked 2 is evidently most likely a salmon whereas the complex decision boundary shown leads it to be classified as a sea bass From Richard 0 Duda Peter E Hart and David G Stork Pattern Classification Copyright 2001 byJohn Wiley amp Sons Inc 22 salmon u sea bass ltghmess 2 4 6 8 10 FIGURE 16 The decision boundary shown might represent the optimal tradeoff be tween performance on the training set and simplicity of classifier thereby giving the highest accuracy on new patterns From Richard 0 Duda Peter E Hart and David G Stork Pattern Classification Copyright 2001 byJohn Wiley amp Sons Inc decision postprocessing 4 costs adjustments for context adjustmean for missing features feature extraction segmentation sensing input FIGURE 17 Many pattern recognition systems can be partitioned into components such as the ones shown here A sensor converts images or sounds or other physical inputs into signal data The segmentor isolates sensed objects from the background or from other objects A feature extractor measures object properties that are useful for classification The classifier uses these features to assign the sensed object to a cate7 gory Finally a post processor can take account of other considerations such as the effects of context and the costs of errors to decide on the appropriate action Although this description stresses a oneiway or quotbottomiup flow of dam some systems employ feedback from higher levels back down to lower levels gray arrowst From Richard 0 Duda Peter E Hart and David G Stork Pattern Classification Copyright 2001 by John Wiley amp Sons Inc start collect data prior knowledge eg irivariarices trairi classifier I I evaluate classi er end FIGURE 18 The design of a pattern recognition system involves a design cycle similar to the one shown here Data must be collected both to train and to test the system The characteristics of the data impact both the choice of appropriate discriminating features and the choice of models for the different categories The training process uses some or all of the data to determine the system parameters The results of evaluation may call for repetition of various steps in this process in order to obtain satisfactory results From Richard 0 Duda Peter E Hart and David G Stork Pattern Classification Copyright 2001 byJohn Wiley amp Sons Inc


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