Learning
Regularity: “Bulldozer computing”
- Nearest neighbors: pattern recognition
- Neural nets: mimic biology
- Boosting: theory
Constraints: human-like learning
- One-shot
- Explanation-based learning
Nearest neighbor
A detection mechanism generates a vector of features. These features are converted in a vector of values that is compared to a library of possibilities to find the closest match in order to recognize patterns, objects, etc.
Method: Standard objects (ex: electric covers) are positioned in a space according to recognizable characteristics (ex: size, hole size). Decision boundaries between the standards are then established in the space to define areas of attribution to a nearest neighbor. Objects are then sorted according to which area the belong to.
Another method of sorting objects (ex: newspaper articles) to recognize could be to compare the angle of their vectors in the space with the vectors of the standard objects.
In the case of a robotic arm, instead of solving equations of angles at the joints which cannot be implemented in real life, due to friction and wear, a table of values is gathered at each position during a learning phase. During the working phase, the closest set of values from the table is used to complete the task.
Problems
- Spread: Values can be concentrated in the space making it difficult to discern objects. Solution: norm the data using statistical analysis.
- Subject matter: make sure to measure values that do actually make a difference between objects, not values that generate confusing results.
- Relevancy: use data that is relevant to the matter at hand, not just any data that is independent from the target results.