Compute Statistics of the Training Population To create a mahananobis distanace classifier we need to deternime the mean and cOvA cvar ( APO oPTn ()m MEANA mear (Apop nI COVB BPOP BPOP MEANB BPOP COVC : cvar(CPOP CPOP MEANC : mean (CPOP COVD DPOP DPOP MEAND =me DPOP The classifier will use the inverse of the covariance matrix, so let's compute it ahead of time INVCOVA=COVA INVCOVB= COVB INVCOVC= COV INVCOVD= COVDCompute Statistics of the Training Population To create a Mahananobis distanace classifier, we need to deternime the mean and cavariance matrices from the population of letters. n := 1.. 25 m := 1.. 25 COVA := cvarº Ø (APOPT) Ænæ ,(APOPT) Æmæø ß MEANA := mean Ø º(APOPT) Ænæ ø ß n, m n COVB := cvar Ø º(BPOPT) Ænæ ,(BPOPT) Æmæø ß MEANB := mean Ø º(BPOPT) Ænæ ø ß n, m n COVC := cvar Ø º(CPOPT) Ænæ ,(CPOPT) Æmæø ß MEANC := mean Ø º(CPOPT) Ænæ ø ß n, m n COVD := cvarº Ø (DPOPT) Ænæ ,(DPOPT) Æmæø ß MEAND := mean Ø º(DPOPT) Ænæ ø ß n , m n The classifier will use the inverse of the covariance matrix, so let's compute it ahead of time. - 1 - 1 INVCOVA := COVA INVCOVB := COVB - 1 - 1 INVCOVC := COVC INVCOVD := COVD 4