RIT DOC, AD-A277 507I,a. Ri I¼ ;?I Y CI SSI r Ir- C

Unclassified .

2a. SECURITY CLASSIFICATION AUTHORITY _Jw -7

aft________ _., krpL.uvea tor puDLic release; distribution2b DECLASSIFICArION /DOWNGRADI ED C 019 unlimited.

4 PERFORMING ORGANIZATION REP M8IER(S) S MONITORING ORGANIZATION FREPORT NUMBER(S)V F 5 #69 o o6a. NAME OF PERFORMING ORGANIZATION 6b OFFICE SYMBOL ila NAME OF MONITORING ORGANIZATION

Institute for Brain and (if appiicabfe)'e - Personnel and Training Research Programs

Neural Systems Office of Naval Research (Code 1142PT)

6c. A'DORESS City, State. and Of-Code) 7b ADDRESS (City, Sadce. and ZIP Cce)

Brown University 800 11orth Quincy Street

Providence, Rhode Island 02912 Arl-iagton, VA 22217-5000

8a. NAME OF FUNDING ISPCNSORING 8b. OFFICE SYMBOL 9. PROCUREMENT INSTRUMENT IDENTIFICATION NUMBERORGANIZATION (if applicable)

N00014-91-J-1316

8c. ADDRESS (City, State, and ZIP Code) 10 SOURCE OF FUNDING NUMBERS

PROGRAM PROJECT TASK WORK UNITELEMENT NO NO NO ACCESS:ON NO

1 1 TITLE (Include Security Classification)

Proceedings of the NIPS'93 Postconference Workshop on Methods for Combining

N Ij~1 M~fitjnrkr'z12 PERSONAL AUTHOR(S)

-- ij-.ael P. Perrone

13a. IYPE OF REPORT [713b. TIME COVERED D14 DATE OF REPORT (Year, Mont. Day) 15. PAGE COUNTFRTechnical Report ROM ro March 15, 1994 51(2-sided)

16. SUPPLEMENTARY NOTATION

17. COSATI CODES 18. SUBJECT TERMS (Continue on reverse if necessary and identify by block number)

FIELD GROUP SUB-GROUP AveragingEnsemble Methods, Committees

05 n

19. ABSTRACT (Continue on reverse if necessary and identify by block number)

TV',is report is the proceedings from the NIPS'93 postconference workshop entitled "Pulling

It '.ll Together: Methods for Combining Neural Networks." The goal of the workshop was to

examine theoretical aspects and heuristic methods for combining existing neural network

algorithms to generate systems which have improved performance properties.

This report includes an outline and summary of the workshop as well as the slides used in

each participant's presentation.

D2TIC Q UAL;'r' • .

20. OISTRIBUTION/AVAILABILITY OF ABSTRACT 21 ABSTRACT 'ECURITY CLASSIFICATION

CLI UNCLASSIFIEDOUNLIMITED 0i SAME AS RPT Cj DTIC USERS Unclassified

22a NAME OF RESPONSIBLE INDIVIDUAL 22b TELEPHONE (Include Area Code) I 22c OFFICE SYMBOL

Dr. Joel Davis l (703) 696-4744

DO FORM 1473,84 MAR 83 APR editon may be used until •xhaulted _ ý kjPiTY (1ASýSI i( AI()j()r4 1OHf ,(

All othor ed•tiOns are obh.olete

94 3 29 024

DISCLAIMII NOTYcl_0ý

\ M\

THIS DOCUMENT IS BEST

QUALITY AVAILABLE. THE COPY

FURNISHED TO DTIC CONTAINED

A SIGNIFICANT NUMBER OF

PAGES WHICH DO NOT

REPRODUCE LEGIBLY.

*~~~~Z j' .Z, m ~ m- -mt -

itt

-E-0E

-x- Fi

f 7c 0~ ~

r, 7t zi 6 2

T" C~ m

c 7 it *

72 it4

-. ~~ - . ii t r C

C~ ~ ~ c-- -

-0

~Z~;-0

o~~~~~~ -ctt-.zizCi

*'C7ý &

C-1

-7. -*71N

U 70

-o-

-(.Oc

I - -1' %r

0? or a?

* -s

fn -

cU5

z- -C-- -C

~ ~o -c C

~ ~1' U ~ z~En

E+

Et

fn 0 0

- - e~ -~ (

-S O~ ~ ~ =

bo~

&~~~~~ E~ i~- -

-~E

rc.l

-CC

CLj

- .0 0E0 C

0 0 0 -= 0 0 0

-ce

oo

0 >

00

'40- .

'~ G. -~ 0 LIc

-~ ~0H ocn

00

C OCT

0) -ho'UC

o 2 2 -

U -0 -, QZ rn ~- t

Ct2fd Z ~ .3

ol

CCl)

v I v I_Iv 1-j-

rj

Z: C L

+ c

oV

+

L~z L4

0

0

w H

.0

- H HH H

H IEL.

0 � __ z�II II

III

s..i HU)

bO '� x

o -�

*G3-�

2 -� -�

H I.�

IIVJ�i -, -

0.

+ .�z-H -�

H

IIH -� -v

'-III L�N1i Iii

U) I��) �-I-n, �

- ��

ii � E-o

Q.� - 0.- C� 0.0. -0� G.)

0

* . I

_- A I- III A I V

S • Al ± Al AIIlV

o ¢- lL - E •

S- - II

* =

E -lV- V'--

I-" C•"' • "-" '

t-, ,

P- 00C

Q0

L

> U50

Cd 'CI C -

-1-0

.~ ~- ce - CN

H 0 0)

LO)) 0)

o bi) v

ce (U

(CLI -oz Frix = *

> T. C

41 4z

.- ,

/i3S

/a*

3SW

000

V¶ V

1.2.2ZT0

w as

4.) 5 C7

cn4

.61=

-owD

.00

moii

0.

a U

C9I

404

"4 .• 0..,

0 -. 6

z90'a k -

E-4-

"Z ........ ...... .........

z... .

0W0

C) * 0

~ 4 0

ctd

xr

Q) C

WD E10 0..

W 0

-B U

~ .~ E

S rJ-~ - ~ S...0

0 z - b

wr -0 1-41-S... ... _

to

tt >~ClC)

0-

0 i -4J ~v4

IL"- 411vVI X1 dj w

C-C) (V -5 I

U ZJ-4J

CM\ >( 4) 0

C ,-.- 6T

II II

5,) -51

QJ d' -'H

('7 014H' •~

-4 J- 73

4Jsa LL (

0 Io Ot .

SR ~ L-3 ~C4-00 wj~ YL

-*ý 0)72 a

a-, (I, Cq-

'14

C4-1 (4ý

7; X

4i (IL'

)< I

4-J

4 r LA Y

GJ4

441

-,U

-41- -j

I- - 0~ -4

ci5- 4U _ o

ý-v yi 4-Jn~

_ 4 J -4 J

y - _-z ýK (I

p•LL

>I

0 o -

-S-5

C-I T4'*- y- ?7 w -4,

G- rI, ~ ~ .

1-4 ~ --- J

XI -

+2 it -

(-Ii

t~-i ,J

L4!, -4 41

-'I-li~~ LL, . -

+4J- I -

QJ~~~~ \1 ~~Q

-4J-

6D1 -\

-P 0

-~-41

L4 0j

- 0P

'-4)

4J _ 3- ~AJ -4J

,~ 241

QjQ

4J )<-)-

d I-

2 -i

CiL' < Cl.[i-- J >e, -

~ -~ H

I '

4j qj r

171-

Q) --

CO1 t 1-1 -

0jO4 WI

qi LL L

qj~ ~LLz-> qd j

LI4 6t)

S XI

LI +

cpQLI

-4Ji

-~ LI

r Il

I I-,5<W4ixi +

C',

L Jo

Ii V S)

NV~

C-+j

ItA

L) vrKj

~Q -4J~

it H

IV4-

'-J ~ i- 1-

xi

QX1~ +

1-'

1+1

Q.4-

-4 ii.i F~- -~ k1 ~ :-n

14U4--41 14.

U 'I-

0

xZ rnI

ILl

cs

-+j

-1 - -p

r're

-414 qj-~ .

4440

Icý I '-77-

4- x II -0

N -. J

I cJ( o

400

rlC'x-

14

14-

L/)

41P

Cqz

III tl

-t to -

oL io eo t l o • L 0o o o ooSOL 00 00 00 00 oo ) o• go *o .o •o oo (' )

0 0

0

Ir I

0l ,0 g.0 (0 0 0 l" 0oI 0go o' 000 01 SO 00 '0 00 0 0 Ot $00 gO ,00Z ,

goo

'-0

i Hi t • Ill n I • ug | II m | a it | i

II F

Ul)

..4 ... ....- -i t 0 10 t

6r ----- Ln to t0 1, 0 -0 00 C0

r r

ZL 0 to to to z' oo

goL il IL

iEll

0 . 0 0 G # 02~0' G O G O 0 ZO 0 0 O Z' S , 0 : ' S O ' 0 0 1

0, o 1 0 go .o zO oo0

I 4I

c~cJ

ii

ti I rI 3

SZ O i O ,O-0 O

0 0

0 0

IGI

0 1 6 0DO

1O00 0100~0 0

to 0 0

s0o 40 o01J to Do0

00 0

0 0 00 0 O 0

9' 9 0 O 0 000 0 0 0 00 0 01 0 go '0 Z0 00 1, t oo 9 0 . 0 0 0

T-ii

ii -t

4J4 > - -. \(-

0 1ý-4 b I

0 40.4~ 0

w) M (1 0 S

)-. e--4

La P4 0)14 .-6D 4-7 5 Q)

W s- -X 0

04 a)) w ..'.40 0..L

L)a Q)v *"

.- 0. s-4 (ncow-c! 01 -4 40(1

C.) 4. O c

W ) 0 Q tQ

4-

ce 0 0e(L

a)o

040

~4-5000 40

P4J

5--

* 4-

00

0 h91. -n

U, I- IC'

o '- ~ 4. -oT

uj 0~ -~ ~ ~WD

II b .0

0 ~ b U2 ~ rw 0

V -~ -- "

0

0 0

4-D .0 0 '-4

P4-

0-4

bOO

-4~

0 >. .a4-

ce 5- 0

00

(1) ct 0 -

a4S

U) -

0) 4P

9)4 0Ux 0.0

24ý44. $.-

0 .6.20 x *4I-6-D.M- 44-4

0 000

bO *4 0CV.U) 4-a 0_6.

-. 6..4 6.4C. )

0. 0 b0 . 04 - -0 x

0 bO0U) 0O 00 0.

0 .=

0 to0 4

0 4-'In)

r~

.46

CI.-

C)CQ0)

co 44

'U Q)cebO

1-

1-4~

"0 z-

>i U 1 4. .,-

cd Q00 40 1. Q)w >

I) Ll.I0A 0-4. w Cl)ý

bb0'

0 0 0 0 6l 0

-H -H

-L c i 06 0ý t

oz C14

-H -H - -H -H

I-

00 0

00

S--.

__ t-j 0

-c$ -0 10 0~ -c) _

ci. .4- 4 r

C)~ C) 4' r

C14.

I I I I I I I I I

C) mc

00* -

000ca. O Co - O~(

Coec

ce•0

"0~

0 ow 0

uCi

PIN

IJ

II

! I*t*J!W V°

Q))

00

- ..0. 4.4-4 ~5-~0 11~~ 'o

CLCl C C.)

)0 $-4CL,. s. C~ C) 4.-j

.0 P5 ' O

c~ - -4

Q) 0

Cd.

-0 Cd5-4 C.) I

oý c) -

C) . 0

f--o

-6.' -

4Q) .)

0 04.C.) h.oC5.4 -

>- >)

CNO -

oh

tko bo-.0

pooQi- a sIc

ch' .4.

3..~. .-. .-

ct Id

14-I4

ý-4 w~

+;

C-4

xI-

x;

:y I I

z ~ ~ ~ ~ ~ ~~I 'X C)-d. e(')S 213=70

Hwý -0 0xg u UI

Orr;

MniOU2ra (IN Sx'g),91N

-ho

bC,,

bj4

0--

>+

C,,

Cf.)

C,, ' -4

.4- -4-

(1) 0,

Ct Q 0

'-'e-

C) cr.)-

-M

IIe

U)

UU) cn j 0HUJ < wLLU U) n Zcl

z < E o

0-00

L LU W< <D 2: W 1 -0 (0C)> L

LL

C',L

0

I-i0 wwc jDCz : 0 o<

m~ cc Loow> -I) w cc -wo ( ( 2 Z 'Z M

LI 10I--0

z

C,)l

ZO

Z U

Z

cr..

LU U)

0

0- 000

0 wR 0W >

= LL

C/) cc

F 'C/) 0l mr0 w J

0

a0

CD MZ-=w E2C/)0 cc. N

0 1f () '- a.) Ec

oo 1-MC

E oa CL N EL- -. --Oa

C4OX a-.C E Cc z 146 I 0l cc 0CD LLw oa- Z 1 00 '- Cc$ 0 LL cc

0 L LCcC- U

o ooLLz0 - -

00)00)00

00)0a)00

zwj10z zC.) U)

N10

wj w

0.1

zo

oz zoo m LULC

LUU

00

LU N 0

0C- ~ C ~ Cz

erj c' -

LLI-

ui D 0z Lz

LUU

wn C/)

CN/

zo-

crn~vWNfl SN83llVd M3N

ONIISO0O9 IVNIVE1O

05 cf0

z 0'

0

z~~ 0 Lmw - 0) 0

0 -0

z c~~0

z

0wI .),

0n

uj LU I-

z 3 C/)

R 4c04'V- C1 m czLU 00 I

LU N

w

Z< 2r

z0

0

0

z

Z N

00

0

coo

LU00J C

c, C

0 0 < N 0CC a:C/) 0

0 I-i 20

ow C/:zLU]

FU 0Uc

0cl 8 W :v l C~ C-4 -r '-1-l

0'-

C,z

LI-

z ~0 c

LI-

w C/) m

z~ LL

w~ L)

uz0. I-

03NIUI~n SNH3I~d 3N-V

DNIISOS (33:lla)

0,zo :a . R, R ,

LL x 0 0

o. C/) -zp Jýr0 W'

Clo WL w iLo l Cl, F-. cnCl) 0D

0

zm

z z znN c LU

w CCIw wU

0 .L

LU 0z C) 0

,-, 0

_j - 1 0J LU 0SLU < LU NLU ~ N~0

ZU < ~0 LU

uj < 0

0 <

oCC

CCC

LU L

LU0 0 00 ~00

N 0 000 0-

-LU LU L

0) 0 N

< R 02

Zi._j 0D.0

D0 a zHL <

0 0 C a0

04cl co C : D . C%.J C>

0 0i 0 0;

R

0

Fwi 0~00

se 0 2 0~

0 0 u 0 F

0

I0 0.

cc DI

(N (N N -4 ,4 ,4 .4~

00

z 00

(0 <

0 0 L)<J (52 N

0 0u

z < E LLJ~z I--

0 ~ a.< U

0 E3 0* 0 Z1*~ z

;20a

0cF1- L

00

0 00 0

0 Cicnco th

zza:o0 cn(n 0

0 c0 C4 lo .

Lm( 0 .

wU N L wz) z 0f0Z Lii Z o

0I 0f IcC6 ccJ m\ 'N di

LU ccZ

0

LLJ

CV Z rC C/)H

0U 0W0

H -J ~C4 zl IW

0w Z >

o 0 Zu0 2 Wujcc l)- > z crzr

Ml Cf) 00z

CC L

A Commercial Application:Extracting Document Content

from Images

Christopher L. ScofieldHarry Chang

Ed Collins

IifINestor, Inc.

Structure of the ProblemOCR is not really a problem of characterrecognition. It is really language processingfrom images:

Character context drives segmentation:

Lexical context drives character interpretation:

iO Nestor, Inc.

Structure of the Problem

Lexical context -"',es character interpretation:

- clone?- done?

Application specific rules drive interpretation:

54Y293?5" d y 72k -3 54Y2A3?

J4Y2A3?

Ii Nestor, Inc.

Structure of the Problem

Document structure drives syntactic and lexical possibilities:

Company Name

Street number Street name

City State Zip

I 7 Nestor, Inc.

What are the possibleapproaches?

Single Network Architecture" [Keeler90]: Combined segmentation and recognition;

,, [Fontaine92]: RNN trained on pixel-columns

- Pluses:

, Makes no assumption about structure of problemAutomatically trains each part of problem

- Minuses:

Scaling problemLack of modularity: application dependent

If iNestor, Inc.

Possible methods

* Multiple Network Architecture" [Gouln92]: Neural network segmentation for map

processing;

, [Scofield92]: Context-driven segmentation,recognition

- Pluses:, Each module can be built in a minimal fashion

Only some parts need to be changed for newapplications

- Minuses:" Assumes prior knowledge, Must be assembled in a piecewise fashion

Credit assignment problem

IFJNestor, Inc.__

A Multiple Network Approach toOCR for Handwriting

Task decomposition:

S~~~ocn. .'Struture--

S.Reog~nition LSegmentation

I lNestor, Inc.

Neural Network Segmentation

• Neural network is used to assemble a treerepresentation of the image:

(1) Classify all blobs Into "Character", "Noise", and "Mixed"

(2) Recursively segment "Mixed" until decomposed intoonly terminal nodes "Character" and "Noise"

(3) Compose a list of possible alternative segmentationsFragmented charactersOptimal window adjustment

F Nestor, Inc. _

Segmentation: Step 1

e 3-layer BPN trained to classify blobs into:"Character" "Noise" "Mixed"

it* * ,o.

e Use connectivity analysis features [Hu62]including:

area, perimeter, number of holes,

area of holes, principle moments,

aspect ratio, etc.

ffl Nestor, Inc.

Segmentation: Step 2S"C",c, "N" are terminal,"M" parsed with quad-

tree analysis [SametO]

Re-classified at each _

step: •j_ J

f 7 Nestor, Inc.

Segmentation: Step 2 (contd):- Hierarchical Agglomerative Clustering [Duda72] groups

terminal nodes; re-classified to ensure still terminal:

Step 3:a List of segmentation alternatives compiled for

classification into characters- Multiple "cuts" of characters provided for later analysis:

IQiNestor, Inc.

Segmentation Accuracy

"* Test set consists of 5,654 HP/MP charactersin 1,236 words (46% HP) selected from 53real-world documents

"* HP data consists of live forms withconstrained HP, unconstrained HP, run-onHP and some cursive

* Character segmentation accuracy:(First choice correctly segmented)

Segmentation Network Correct Incorrect

"Blob" features (7-10-3) 92.7 7.3

I 7 Nestor, Inc.

Character Recognition:Overview

* Segmentation alternatives are processed forcharacter class

° Use two static feature sets: (cf - [LeCun90])"° Three-layer, feedforward BPNs are used as

estimators of a posteriori probabilities"° We have employed three types of hybrid

networks:- Glue networks [Waibel88]- Parallel Experts [Reilly87]- Hierarchical Filters [Reilly87]

\ffO Nestor, Inc.

Character Recognition:Feature Extraction

"* Segmentation alternatives are converted togrey-level: gaussian kernel estimated from linewidths

"* Pixel Feature Set:- Pixel map is sub-sampled with grid producing coarse

map (100-element grid)

"° Edge Feature Set:- Edge-map produced from grey-level gradient estimation

[Roberts65] (4 edge directions)

- Edge map is sub-sampled with grid producing coarseedge map (30-element grid)

If lNestor, Inc.

Character Recognition Classifiers° Features used to train 3-layer, feedforward BPNs

Data Set # Authors Digits Alpha U/LC

Train: NIST 1,3; Propr. 2600 265,000 120,000

Test: Propr. 4,767 12,932

In addition to using "Forced accuracy", can use aheuristic which models high cost of errors:

"Figure of Merit": FM = 100 - 10(%E) - %R

Numeric Network FM Correct Inc. Reject Forced

Edge features (120-32-10) 95.15 97.04 0.21 2.75 99.01Pixel features (100-45-10) 93.41 95.87 0.27 3.86 98.59

FflNestor, Inc.

Classifier Analysis

e Using "rule-of-thumb" e = W/T [Baum89]:

Training set: T - 265,000

Edge Net: W - 120*32+32*10 = 4,160Expected test error: e = 1.7%

Pixel Net: W = 100*45+45*10 = 4,950Expected test error: e = 1.9%

fi Nestor, Inc.

Character Recognition:Parallel Experts

"* How to combine the results from two networks?

"* Could vote if have many "experts'. If only two, thenaverage activation (probability) vectors:

Pi = 1/2(Pei + PP1) 0

Numeric Network FM Correct Inc. Reject ForcedEdge features (120-32-10) 95.15 97.04 0.21 2.75 99.01

Pixel features (100-45-10) 93.41 95.87 0.27 3.86 98.59

Parallel Nets: 97.25 98.20 0.10 1.70 99.39

IFiNestor, Inc.

Alphanumeric Character Recognition

"* Support full alphanumeric HP"* Natural decomposition into alpha and

numeric subnets

" Use glue-net architecture [Waibel88]:- Trained 3-layer nets for alpha (u/I case) and numeric

- Freeze middle-layer weights, route activations to output- Add-in new "glue" layer to resolve Inter-class ambiguity

- Train second layer of weights and all glue-cell weights

alpha numeric alpha numeric alphanumeric

[ iNestor, Inc.

Alphanumeric Accuracy* Results: Forced

Network Architecture FM Correct IncorrectNumeric sub-net (120-32-10) n/a 95.15 4.75Alpha (U/L) sub-net (120-120-26) n/a 91.41 8.59Single Glue Net(l) (120-210-36) 51.24 89.24 10.76

Hierarchical (Super) Glue Net 54.04 90.80 9.20Single Glue Net(2) (120-210-36) 54.46 89.98 10.02

SIumeric

Pixel features

Edge features

fIINestor, Inc. Eg~et

Glue Net Analysis

Digit set: 265,000Digit Net: W = 120*32+32*10 = 4,160Expected test error: e = 1.7%Alpha set: 120,000Alpha sub-net: W = 120*120 + 120*26 = 18,720Expected test error: e = 15.6%Full set: 385,000Glue weights: W = 120*58 + 210*36 = 14,520Expected test error: e = 3.8%

iONestor, Inc.

Character and Lexical Context

Word recognition: determine the best stringinterpretation given all sources of knowledge:

segmentation alternativescharacter recognition probabilitiescharacter transition probabilitieslexical context

el~o-n-e O --V 1 D7cl 0/m--P D H U"I \P

Character and Lexical Context

"* Use the Viterbi algorithm [Viterbi67, Forney73]to select the character string with maximum aposteriori probability

"* Let maximization of word probability driveproper segmentation [Bozinovic82]

"* Problem: VA can produce lexically incorrectstrings. Post-processing with a dictionary canproduce word which is not MAP.

"* Solution: Use lexical context to trim pathsfrom VA search ensuring that the final string isboth MAP and lexically correct (Srihari83].

IO Nestor, Inc.__ _

Application Context Processing" Some applications are alphanumeric but not

part of lexicons: A1800138- Inter-character statistics are specialized, hard to learn

without large set

"* User-definable syntax selects which subnet touse for each character position, trimssegmentation alternatives to match syntax

ZIPCode

(FiN es tor, Inc.__ __ _ __ __ _

Status and the Future

* This architecture is the basis for the productNestorReader

* To be supported on Nil 000 neural net chip

* Extensible to character-based cursiverecognition

- Now developing much larger training and test sets for run-on HP and cursive

- Developing stats. on character and segmentationaccuracy due to character and lexical context

IO Nestor, Inc.

L4--

"4- ~ ..zl

Is

44

I-'1

LLb

0041 0061 OCSlt oss 0

Wddl

out o t OGGt 096

I0

L~ddi

v 40la, CC1 Vd OI

see$ 006 OS 0L6 I ~SWL 096 L m 1 06

LA

(cc~7~'A ~ 'A~?~

IL

ID

0..

a - - CL0 I

I. U(

on

3ONYHO~~~ ~ ~ ~ ~ IN18d3NH N03 0Y)C3@

%WdI

,addA

aA IX

"I*-

Lu

a S

1.4 -0 -TO"a

-a-AR(14) hewraed/-a-Unew Rg.

0 1.2 pa Net

* 0.8

0 2 4 6 8 10 t2

Prediction Honizon (Months)

-cP .- ,".~ , ýy ve s t";

.~pb

-c dS9

3 'C ~x *-

.2"z z i ~ e i l l , Ict wa?5.1n;2 x:.9

'11 - . I 1 i>1________ r il

ii _______goo

dzIQ 3

y L4

Up x

'J. ~A

L44-

oc 9z 0.1 SLO

9,

II

Pq 4-A .

J9SA

99~ 99z10

~AM

JeeAS

9z

*~ ~0

9'0

jes Buiuieal jv9A L k

Ip -- 7yr training committee vs average

0.65 -

Drawsocomijt~tee -

0.6

0.45 / ,0.55

0.35

0.3 /

0. 25 t'

0.2 ""

0.15 'so 82 84 86 as 90

r. ÷u• a- n,,, v, C..ess.1

IP -"7yr train'ing :coxmmittee vs averag'e0.65, ,,

r ]aw-

0. 6 "commit tee". ..

0.55 '

0.50.45 / '

0.4 / ,

* S

0.35,'"

0. 3 ,'

0.250.' "

0.15 ,

80 82 84 86 88 90

•,,' • •; " _•A )-

•." ,-, . . - /, . "

G4..

,'

N NL

V -N

N Z -

74A.

'S4

zSj"S

-L

\lLk ,

1';;ý /4LA,

K.L

' ' \

~-\ ~ -3-11

- . ,I-.

, ) , ¢ .

1,,

S - - . • • • • sal '-i-a i i i• I I II Ii I ws

I% V

* -S 44 I

'.4-. �'

-V.. 4. t (1%�

0� '+,� 0

6) S., 44.4.. at c *

4 9%. I,

0 *�Ub C � 4-

'a 4OD

-� 4 '1.10

GD

k

cr�

9r0

4

'I'I

V

'4.

61

4

Fb.1;'4,'a6)

(A

C C 4�� E4-� -

�1% � SI

.4- -

SI SD 'Sfri � Esm. � e.3 4

4�. 6 ' � �m.4.. 4 SI

'a* SI � 4 � .,O 4*

'. 8. IS 4ID) * q� � I-,- *

'F *� C4�' � -I-

.% SD

E *! �.g1.�

S.. fri *.3 em..4 -.�.

'-.-.Z�- C

4- C �., 0

-m-m--E.-�--- - .m.� *,� �* IL1 � * '

"1 i- 34

*0-30

9%'C4 .� .�

I'.'. C 6 C -.3 6

.�* *% qL�. C �

'S. .* -� d1.. * 4 *1�

6 C

1d� jh d £

4%, .�.4

1 Sq34 �. %%6 6 \� I-

0 .� Ti �

N £ 6 6�7'- d j��4

V N -. 0 .. .-. .. 0.. � I ,-�0 Va 0 C

*4D %.. 'p *b

9 %%

ID Sb

0 0 0 0 * "I

"SI .

4 Ii, 4� ¶1V *0 * -

olu 0� 4, *'� �

U + �SD Q

£V .. 4

-44 SD,) �

( g% * bl

C �D *i' .¶�N . 4 .4

�mim. �4V ( UGD.,. 4

urn6 .- �.. .�

GD X9 1- �

� a *V% o-te %.

Qb 3 ti�. 4�*- 0

(Si

GD� -,-

'p .5

9'I

I

a

b �: �J1

( . - (Id aI Lb1'

S

*1'I

'. 'p.

'PD C.%

j C

� -

f) �-.

04

CCo

0-zU

Cu _ 0

Sal cu.s c0 Q 0 1 6

Qu c -ý 0 *ý$ -P

Poi1 No-

N0UZI

_~ Eu

g u

E CL

-0 M 0- 0

CCL)

o -2

0. 0

.6- -6-o .6.2

ga. bO 0:Id

.., 0 %0-4

- 0FE -4- >

0- =1I -- 6.

CIS , w ~ w.Q) bO 00 ~2~'.o xi,~.

00 'H 0e,_0

o am

0 I,

/ o1$-4 W Lo

CILI

o

$-4$-4

0)4.2 &4~;

b\ 0

06.

w w0 0bOe 0

0 0 0

bOO

Q ID

S CZ)

6- V

ce ~ .2 8$- go,-

a)o ;p ,. a

W~ Q)

.40v 1; it!

*I-

P4 -12*

Mi! V

fil

.6.

0 b

1.0

%- 40

-CC.

CGO)

0

(V 4tMl A. C

gy 0-t- ý

i tip

'/14( 4 * 3. . .. ... ..

3Q '16 1,- 0 vCf

.%b4

I-6Q

""A CL4~

0 40

-10

-4.2,'-

Q) k

0 0

0 C) 0

z-f.

-- - G�

- -

- C- � -- -

- - - � C-- -� - -

- - 0 0 -,-. - -0 -

4-- - 0 C - - cC

- - C - -

* C - -

C � o _

o I I I I - I I C* 0 0 0

Cl)

0

z

� z�C/) -

-s Lz.� --� � -r(r� -�

0 � -� ��z C,) --5

Q� �

QOHCl) 0

I

C- �-

C�

oU, -

z0 �

- 7�N� 0

� a* * IIo � H�1-

0 -

7K �4iTH

i-i .� -�--� C.)'I K

�--< 0

- o� 0�v 0.

o 0.I-5-

0)

Q z0 S

- I

�'4

o £

z -

oU,

� Z 00

I-0 -U � �')\S..-.

- / -- II

0 Cu-� "� I> C I

z -

0 C-

0).0 -�

Al

0 -, - AlZ : I - II '-

"or¢ - -"

cj.• .- = -

- II I 6'I

0

z - '- C -,,Y -- l

• - - I.I

- I . -Z

- Z Tit

o - -: - " -. _

zz

- 1.0II

zO - C� £

� -

� Q0 � -U

z

C

0

Co 0 0 C

.7

+I.

- * I -

:- -

2: - £

2: - -� -

2: --

0 � -

Q Cz� 'N - -I- 2 7 *. I-

- -�

0 � - '**�=Cl) c

E-� �.4 CS =

�L) b.� I - -� - .* -� .*

- +'N - � -�

0 '-' - - '- -

�I.- .� -� . �

0 � .- .- �

�0 2- II k � � �

0 - Z U)o� - I I* 0 0

I'

I'

_ / C

�

2:0 4� E-' 0

2:0 � *1

z

o :� .1

C

C

0

- = -

I.zz. -

-r�i) -

z �- -

I.'4 - -

* 'N

00 � = -

-� r- - 'N

Cl) � C 1 'N =-� U) �

'N - -

- - _ -' ZN�Ii

� -� � $- f

� �- � 00 -

� �oI I I Iz

* 0 0

S

0�

- .f.

0

� 0

o � C

0

CC -

- C C -

U2

u-I

(*� 0 -�

�

C � -" INQz � -� - U.2

I -. -C- - - f � e - -

�-I � ..- - -. � - - _

-� C/� IN IN = (Zp2.. � I �

- - -� zZ 0 -= �rI� 7 1-. � -�

- H -� *� �' I.

A � -� � �0 ;.z �- � .� *� � -4-a -

0 -.

0 � � *�

� �2 k E*-� &-. .� -� �z -� o�

I I I0 0 0

S (

7Z C-

1.--C

x ..

1 4.fw T .- w w

- - V)

C- -

zn L -

Q~ 00, U*

z .-

~E F-