Tag: learning
17. Learning: Boosting
“Wisdom of a weighted crowd of experts” Classifiers Classifiers are tests that produce binary choices about samples. They are considered strong classifiers if their error rate is close to 0, weak classifiers if their error rate is close to 0.5. By using multiple classifiers with different weights, data samples can be sorted or grouped according to different…
16. Learning: Support Vector Machines
Decision boundaries Separating positive and negative example with a straight line that is as far as possible from both positive and negative examples, a median that maximizes the space between positive and negative examples. Constraints are applied to build a support vector (u) and define a constant b that allow to sort positive examples from…
15. Learning: Near Misses, Felicity Conditions
One-shot learning Learning in human-like way, in one shot: learning something definite from each example. The evolving model Comparing an initial model example, a seed, with a near miss or another example, the evolving model understands an important characteristic for each new near miss or example compared. The evolving model develops a set of heuristics to…
14. Learning: Sparse Spaces, Phonology
Visual recognition to sound production Words can be classified in tables representing the 14 features of each sound, notably if they are “voiced” or not. With such a matrix of sounds, a “speaking machine” can be built to produce words and sentences. Example: a machine pronouncing plurals The machine processes from recognition through different characteristics…
13. Learning: Genetic Algorithms
Mimicking chromosomes Reproduction loop Strings of 0 and 1 simulate chromosomes in binary systems. An initial population of chromosomes is allowed to mutate (modification of certain bit in the strings), cross over with other strings during replication, or to remain the same. Mutation: enables a step by step modification of chromosomes, allowing hill-climbing to improve…