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Therefore, the construction of the classi er takes O(TFq log q) Wu s [30] method takes O(Fq log q) time to train all of the classi ers in the initial stage Testing each new WC while assuming that the summary votes of all classi ers are previously stored would take O(q) time It would then take O(Fq) time to select the best WC Therefore, it takes O(TqF ) time to chose T WC We deduce that it would take O(Fq log q + TqF ) time to complete the training using the methods described by Wu et al [30] The dominant term in the time complexity of Wu s [30] algorithm is O(TqF ) This is order O(log q) times faster than the training time for Viola and Jones method [27] For a training set of size q = 10, 000, log2 q 13.

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Since HEDFt is random, this implies that CEDFt is random, and is calculated from the realization of the standard normal random variable in the simulation, Wt (see next section). If we de ne G to be the mapping using the empirical distance to default distribution over time from the current distance to default6 to the conditional forward default probability HEDFt, the forward distance to default is given by

For the same size training sets, Wu s [30] algorithm would be 13 times faster to train, not a 100 times as claimed by the authors The authors compared training times to achieve a predetermined accuracy rate, which requires fewer training items than Viola and Jones method [27] Froba et al [13] elaborate on a face veri cation system The goal of this system is to be able to recognize a particular person based on his/her face The rst step in face veri cation is face detection The second is to analyze the detected sample and see if it matches one of the training examples in the database The mouths of input faces into the system are cropped because the authors claim that this part of the face varies the most and produces unstable results They however include the forehead since it helps with system accuracy.

C rENU 1U 1Y ;

The nal piece of anatomical knowledge which is helpful for understanding the neurological control of the left leg, is a little more detail about motor and sensory representation in the brain. The important features to remember here are: the motor cortex is in front of the central sulcus, and the sensory cortex is behind it; the body is represented upside-down in both the motor and sensory cortex; the axons of the upper motor neurones in the precentral motor cortex funnel down to descend in the anterior part of the internal capsule; the axons of the 3rd sensory neurone in the thalamus radiate out through the posterior part of the internal capsule to reach the postcentral sensory cortex.

The authors use the same training algorithm for face detection as Viola and Jones [27], but include a few new features They use AdaBoost to do the training, but the training set is cropped, which means that the machine is trained on slightly different input than Viola and Jones [27] The authors mention that a face is detectable and veri able with roughly 200 features that are determined by AdaBoost during the training phase The actual veri cation or recognition step of individual people based on these images is done.

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PSYCHOLOGICAL ASSESSMENTS FOR THE COURTS Lipsitt, PD, Lelos, D and McGarry, AL (1971) Competency for trial: A screening instrument American Journal of Psychiatry, 128, 105 109 Loftus, EF (1979) Eyewitness testimony London: Harvard University Press Lord Chancellor s Department (1997) Who Decides Making decisions on behalf of mentally incapacitated adults London: The Stationery Of ce Lord Chancellor s Department (1999) Making decisions London: The Stationery Of ce McAllister, TW (1981) Cognitive functioning in the affective disorders Comprehensive Psychiatry, 22 (6), 572 586 McCabe, MP (1999) Sexual knowledge, experience and feelings among people with disability Sexuality and Disability, 17, 157 170 McCabe, MP and Cummins, R (1999) Sexual knowledge, experience, feelings and needs of people with mild intellectual disability Mental Retardation and Developmental Disabilities, 13 21 McCarthy, M (1999) Sexuality and women with learning disabilities London: Jessica Kingsley McCarthy, M and Thompson, D (1998).

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