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<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999>JimG</SPAN></FONT></DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN class=840193101-25031999>Even a
blind squirrel can stumble over an acorn in the forest once in a while.
<BG></SPAN></FONT></DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN class=840193101-25031999>After
the way the market dropped, I worried that I had taken my profits too
soon. Now it's back to 1.40 points of where I got out (basis close), so I
won't have to listen to my brother about pulling the trigger too
fast.</SPAN></FONT></DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN class=840193101-25031999>The
more I think about it, grabbing those early profits when trading against our
intermediate term trend makes a lot of sense. At least you grab the money
and don't have to sit there and watch those paper profits evaporate. This
was a real hard decision for me as it's contrary to our paradigm that we've
established over the last 40 years or so. Always in the market (unless
stopped out), either long or short, but always in. I guess all of us can
learn. Meanwhile, back with the 8 year old...</SPAN></FONT></DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN class=840193101-25031999>Took
Evan and the new puppy to the doggie park and tried to tire them both out.
Finally got to look at my e-mails around 4:30 PST. I don't know if I can
make it through a 2 week Spring Break. <G> Tomorrow's another busy
day with no time to work on my stuff. Porsche and BMW are both going in
for service (BMW providing the loaner) and then off to the new aquarium in Long
Beach. Friday will probably be Universal Studios since all of the other
schools are still in session. I'm tired just thinking about
it.</SPAN></FONT></DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999>Regards</SPAN></FONT></DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999>Guy</SPAN></FONT></DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999>Regards</SPAN></FONT></DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999>Guy</SPAN></FONT></DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999>Guy</SPAN></FONT></DIV>
<DIV><FONT color=#0000ff face=Arial size=2><SPAN
class=840193101-25031999></SPAN></FONT> </DIV>
<DIV class=OutlookMessageHeader><FONT face="Times New Roman"
size=2>-----Original Message-----<BR><B>From:</B> owner-metastock@xxxxxxxxxxxxx
[mailto:owner-metastock@xxxxxxxxxxxxx]<B>On Behalf Of</B> Jim
Greening<BR><B>Sent:</B> Wednesday, March 24, 1999 7:08 PM<BR><B>To:</B>
Metastock<BR><B>Subject:</B> 50% Cash<BR><BR></FONT></DIV>
<DIV><FONT color=#000000 size=2>All,</FONT></DIV>
<DIV><FONT color=#000000 size=2> I was stopped out of
AMZN and WMT this morning. I didn't like the look of the market so I
decided to go to 50% cash and also closed AOL and SCH. Judging by the
close, I may have over reacted and made a mistake. The good news is that I
can always get back in <G>.</FONT></DIV>
<DIV><FONT color=#000000 size=2> Guy, that was a great
short call. Let me know when you go back long.</FONT></DIV>
<DIV><FONT color=#000000 size=2></FONT> </DIV>
<DIV><FONT color=#000000 size=2>JimG</FONT></DIV></BODY></HTML>
</x-html>From ???@??? Thu Mar 25 04:30:26 1999
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From: "Walter Lake" <wlake@xxxxxxxxx>
To: "Metastock bulletin board" <metastock@xxxxxxxxxxxxx>
Subject: pattern recognition book and software
Date: Wed, 24 Mar 1999 20:55:14 -0500
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http://www.unica-usa.com/products/solvbook.htm
Book: Solving Data Mining Problems through Pattern Recognition
A Recipe for Pattern Recognition: A Book No Practitioner Should Do Without
Published by Prentice-Hall, ISBN# 0-13-095083-1, as part of the Data
Warehousing Institute Series.
This comprehensive, 300-page book is written exclusively for people
interested in applying neural networks and other data-driven techniques to
real-world problems. Regardless of your current level of expertise, Solving
Data Mining Problems through Pattern Recognition will help you gain
practical skills and theoretical insights into solving problems using
"non-parametric" methods. The book provides a practical perspective on the
nature of pattern-recognition problems and presents a systematic framework
for solving them. Key concepts are explained clearly and intuitively, in an
easy-to-understand fashion.
Solving Data Mining Problems through Pattern Recognition walks you through
the entire process of solving problems using data - from problem definition
to data preprocessing to selecting the right algorithm to training and model
validation. Recommended techniques from this book will save you significant
time in experimentation and can help you avoid common pitfalls when using
neural networks. In addition, examples of how these techniques can be
applied using Unica's Pattern Recognition Workbench are shown to provide a
practical perspective on the technology. Written by expert consultants in
applied neural networks and data-driven techniques, this book will help you
gain an understanding to time-proven techniques that work.
Concepts Covered Include:
Theoretical objectives behind pattern recognition
Framework for solving pattern-recognition problems
Algorithms such as linear regression, logistic regression, unimodal
Gaussian, multilayered Perceptron neural networks, backpropagation, radial
basis functions, K-nearest neighbors, Gaussian mixture, nearest cluster
Training, testing, evaluating models
Cross validation, bootstrap validation, sliding-window
validation/rolling-window regression
Table of Contents
Introduction
Basic Concepts: Classification
Basic Concepts: Estimation
Additional Application Areas
Overview of the Development Process
Defining the Pattern Recognition Problem
Collecting Data
Preparing Data
Data Preprocessing
Selecting Architectures and Training Parameters
Training and Testing
Iterating Steps and Trouble-Shooting
Pricing
Solving Data Mining Problems Through Pattern Recognition is available in
bookstores, or you can purchase a copy here for $49.95 plus $6.50 shipping
and handling (for orders in the US, shipped UPS) plus New Jersey state sales
tax if applicable. Visa, Master Card, and American Express accepted. Please
contact Unica Technologies at (781) 259-5900 to order your copy. Or click
here to fill out an online form to order a copy.
Customer Quotes
Solving Data Mining Problems Through Pattern Recognition has sold hundreds
of copies and has received rave reviews since its debut. Click here to see
what readers have to say about it!
==========================================
http://www.unica-usa.com/products/prodinfo.htm
PRW PRO+ Software
PRW PRO+ is the top-of-the-line PRW software, with maximum flexibility,
power, and deployment options. A complete, integrated environment for
generating problem recognition solutions.
Company: Unica Technologies, Inc.
Address: 55 Old Bedford Rd., Lincoln, MA 01773 USA
Phone, Fax: (781) 259-5900, (781) 259-5901
Email: unica@xxxxxxxxxxxxx
Basic capabilities:
Supported architectures and training methods include backpropagation, radial
basis functions, K nearest neighbors, Gaussian mixture, Nearest cluster, K
means clustering, logistic regression, and more.
Experiment managers interactively control model development by walking you
through problem definition and set-up;
Provides icon-based management of experiments and reports.
Easily performs automated input feature selection searches and automated
algorithm parameter searches (using intelligent search methods including
genetic algorithms)
Statistical model validation (cross-validation, bootstrap validation,
sliding-window validation).
"Giga-spreadsheets" hold 16,000 columns by 16 million rows of data each (254
billion cells)!
Intelligent spreadsheet supports data preprocessing and manipulation with
over 100 built-in macro functions. Custom user functions can be built to
create a library of re-usable macro functions.
C source code generation, DLLs, and real-time application linking via
DDE/OLE links.
Interactive graphing and data visualization (line, histogram, 2D and 3D
scatter graphs).
See the PRW product highlights chart for highlights of the PRW product.
Operating system: Windows 95, Windows NT
System requirements: Intel 486+, 8+ MB memory, 5+ MB disk space
Approx. price: software starts at $9,995.00 (call for more info)
Solving Data Mining Problems through Pattern Recognition text book: $49.95
Money-back guarantee
Comments: Pattern Recognition Workbench (PRW) is the first comprehensive
environment/tool for solving pattern recognition problems using neural
network, machine learning, and traditional statistical technologies. With an
intuitive, easy-to-use graphical interface, PRW has the flexibility to
address many applications. With unique features such as automated model
generation (via input feature selection and algorithm parameter searches),
experiment management, and statistical validation, PRW provides all the
necessary tools from formatting and preprocessing your data to setting up,
running, and evaluating experiments, to deploying your solution. PRW's
automated model generation capability can generate literally hundreds of
models, selecting the best ones from a thorough search space, ultimately
resulting in better solutions!
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