Predictive learning
This technique finds application in many areas, including neuroscience, business, robotics, and computer vision.
This concept was developed and expanded by French computer scientist Yann LeCun in 1988 during his career at Bell Labs, where he trained models to detect handwriting so that financial companies could automate check processing.
[2] Starting out as a mathematical concept, this method expanded the possibilities of artificial intelligence.
It was inspired by Jean Piaget's account of children constructing knowledge of the world through interaction.
Gary Drescher's book Made-up Minds was crucial to the development of this concept.
[3] The idea that predictions and unconscious inference are used by the brain to construct a model of the world, in which it can identify causes of percepts, goes back even further to Hermann von Helmholtz's iteration of this study.
Another related predictive learning theory is Jeff Hawkins' memory-prediction framework, which is laid out in his book On Intelligence.
Similar to ML, predictive learning aims to extrapolate the value of an unknown dependent variable
In order to predict the output accurately, the weights of the neural network (which represent how much each predictor variable affects the outcome) must be incrementally adjusted via backpropagation to produce estimates closer to the actual data.
This error function is used to make incremental adjustments to the model's weights to eventually reach a well-trained prediction of:[4] Once the error is negligible or considered small enough after training, the model is said to have converged.
In some cases, using a singular machine learning approach is not enough to create an accurate estimate for certain data.
Ensemble learning is the combination of several ML algorithms to create a stronger model.
An ensemble learning model is represented as a linear combination of the predictions from each constituent approach, where
In order to update an unadjusted predictor, it must be trained through sensorimotor experiences because it does not inherently have prediction ability.
[5] Computers use predictive learning in spatiotemporal memory to completely create an image given constituent frames.
[citation needed] Using predictive learning in conjunction with computer vision enables computers to create images of their own, which can be helpful when replicating sequential phenomena such as replicating DNA strands, face recognition, or even creating X-ray images.
In a recent study, data on consumer behavior was collected from various social media platforms such as Facebook, Twitter, LinkedIn, YouTube, Instagram, and Pinterest.
The usage of predictive learning analytics led researchers to discover various trends in consumer behavior, such as determining how successful a campaign could be, estimating a fair price for a product to attract consumers, assessing how secure data is, and analyzing the specific audience of the consumers they could target for specific products.
