What is Pattern Recognition?
Pattern recognition is a process of finding regularities and similarities in data using machine learning data. Now, these similarities can be found based on statistical analysis, historical data, or the already gained knowledge by the machine itself.
A pattern is a regularity in the world or in abstract notions. If we discuss sports, a description of a type would be a pattern. If a person keeps watching videos related to cricket, YouTube wouldn’t recommend them chess tutorials videos.
Examples: Speech recognition, speaker identification, multimedia document recognition (MDR), automatic medical diagnosis.
Before searching for a pattern there are some certain steps and the first one is to collect the data from the real world. The collected data needs to be filtered and pre-processed so that its system can extract the features from the data. Then based on the type of the data system will choose the appropriate algorithm among Classification, Regression, and Regression to recognize the pattern.
https://www.analyticsvidhya.com/blog/2020/12/an-overview-of-neural-approach-on-pattern-recognition/
Filtering and pre-processing means identifying exactly how the training data fits the data-categories for which the neural network is to be trained.
I'll ask it one more time: How do you think the computer system gains the initial information that a certain picture represents a certain thing? It does not possess innate knowledge. It only knows what it has been told specifically. I know how it's done, its done by training up the system using a training dataset in which the data is identified. The classic example is the mine-rock discriminator. Sonar profiles of "known mines" are fed into the system. Along with sonar profiles of "known rocks". These are pre-categorized by the developers. After that, the neural network is fed novel data, which it then attempts to categorize. If it is wrong, the error is "back-propagated" across the network to correct the "weights" of the hidden-architecture neurons. And
this back-propagation is ALSO a manual function, since the computer does not know that it is making an error.
Training an Artificial Neural Network.In the training phase, the correct class for each record is known (this is termed supervised training), and the output nodes can therefore be assigned
Yes, supplied by external sources, not by the researchers. There, the fourth time. — Lionino
The people developing the neural net (aka the developers)
are the external sources. Who else do you think, the neural-net police? The bureau of neural net standards? Jeez. Here's wikipedia on
Labeled_data
Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags. For example, a data label might indicate whether a photo contains a horse or a cow, which words were uttered in an audio recording, what type of action is being performed in a video, what the topic of a news article is, what the overall sentiment of a tweet is, or whether a dot in an X-ray is a tumor.
Labels can be obtained by asking humans to make judgments about a given piece of unlabeled data. Labeled data is significantly more expensive to obtain than the raw unlabeled data.
Anyway, to the OP in general, I think I've conclusively and exhaustively demonstrated my point. And illustrated the very real dangers of a naive techno-optimism. If anything, we should be constantly tempering ourselves with a healthy and ongoing attitude of informed techno-skepticism.
One final cautionary. I worked as a technical expert in the health-care industry until this year, so I've seen a couple of these studies circulated on "baked-in" AI bias.
For example, if historical patient visits are going to be used as the data source, an analysis to understand if there are any pre-existing biases will help to avoid baking them into the system,