Pattern Classification: ch.1 Introduction

Pattern Recognition
; the act of tacking in raw data and making an action based on the "category" of the pattern
- evolving highly sophisticated neural and cognitive systems


Machine Perception

http://en.wikipedia.org/wiki/Machine_perception
: the ability of computing machines to sense and interpret images, sounds, or other contents of their environments, or of the contents of stored media

http://en.wikipedia.org/wiki/Machine_vision
machine vision most often requires also digital input/output devices and computer networks to control other manufacturing equipment such as robotic arms. Machine Vision is a subfield of engineering that encompasses computer science, optics, mechanical engineering, and industrial automation.

machine vision systems use digital cameras, smart cameras and image processing software to perform similar inspections.

Machine vision systems are programmed to perform narrowly defined tasks such as counting objects on a conveyor, reading serial numbers, and searching for surface defects.

http://en.wikipedia.org/wiki/Speech_recognition



Pattern Recognition

http://en.wikipedia.org/wiki/Pattern_recognition
Pattern recognition is a sub-topic of machine learning. It can be defined as

"the act of taking in raw data and taking an action based on the category of the data".

Most research in pattern recognition is about methods for supervised learning and unsupervised learning.

Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. This is in contrast to pattern matching, where the pattern is rigidly specified.

optical character recognition

http://en.wikipedia.org/wiki/Optical_character_recognition
the mechanical or electronic translation of images of handwritten, typewritten or printed text (usually captured by a scanner) into machine-editable text

pilot (adj) experimental

models - descriptions - mathematical in form (*분류할 대상의 종류들 classes 사이의 차이점을 수학적으로 기술한 것)

pattern classification

1) to hypothesize the class of the models

2) to process the sensed data to eliminate noise (not due to the models)

3) to choose the model that corresponds best for any sensed pattern

preprocessing - to simplify subsequent operations without losing relevant information

feature extraction - to reduce the data by measuring certain "features" or "properties"

classification - to evaluate the evidence presented and make a final decision

http://en.wikipedia.org/wiki/Information_flow

 

training sample (*feature로 가정한 변수(예. 길이)의 threshold를 설정하기 위하여 전체 입력 데이터 중에서 선택하여 변수에 대한 측정값을 얻는 데 사용하는 일부의 데이터)  

http://en.wikipedia.org/wiki/Data

cost