Category: Theory

  • 18. Representations: Classes, Trajectories, Transitions

    Vocabulary In a semantic net, a diagram of relations between objects, essential notions can be defined as follows: Combinators: linking objects together Reification: actions implying results Localization: a frame where objects and actions happen Story sequence: a series of actions happening linearly in time Classification In natural language, knowledge is generally organized from general categories…

  • 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…