Spaced repetition: research background. Repetition spacing in learning was in the center of my research over the last ten years (for review see: Wozniak 1990).
In this chapter I would like to familiarize the reader with the concept of the optimum spacing of repetitions that will frequently be referred to throughout the dissertation. Research background There has been a great deal of research on how different spacing of repetitions in time affects the strength of memory and how the resulting findings could be applied in the practice of effective learning. It has been predicted, and to a large degree confirmed, that by changing the spacing of repetitions, a substantial gain in the effectiveness of learning might be obtained (e.g., Bjork, 1979; Glenberg, 1979; Glenberg 1980; Clifford, 1981; Dempster, 1987; Bahrick, 1987). Memory strength and its components. 1.
S/R Model of memory 10 years ago we published a paper that delineated a distinction between the two components of long-term memory: stability and retrievability (Wozniak, Gorzelanczyk, Murakowski, 1995). The paper provided the first outline of the so-called S/R Model that makes it easier to build molecular models of long-term memory. Maximum Likelihood. Maximum likelihood, also called the maximum likelihood method, is the procedure of finding the value of one or more parameters for a given statistic which makes the known likelihood distribution a maximum.
The maximum likelihood estimate for a parameter is denoted For a Bernoulli distribution, so maximum likelihood occurs for . People.physics.anu.edu.au/~tas110/Teaching/Lectures/L3/Material/Myung03.pdf. 20 rules of formulating knowledge in learning. Knowledge structuring for learning. We will discuss elements that, independently of the repetition spacing algorithm (e.g. as used in SuperMemo), influence the effectiveness of learning.
In particular we will see, using examples from a simple knowledge system used in learning microeconomics, how knowledge representation affects the easiness with which knowledge can be retained in the student’s memory. The microeconomics knowledge system has almost entirely been based on the material included in Economics of the firm. Theory and practice by Arthur A. Thompson, Jr, 1989. Some general concepts of macroeconomics have been added from Macroeconomics by M. Knowledge independent elements of the optimization of self-instruction Before I move toward representation of knowledge, I would only shortly like to list some other principles of effective learning that are representation independent:
Amazon Web Services. ProjectsUsingJUNG – jung. Expectation-maximization (EM) algorithm. Ai.stanford.edu/~chuongdo/papers/em_tutorial.pdf. Www.ee.washington.edu/research/guptalab/publications/EMbookChenGupta2010.pdf. Inside-outside algorithm. In computer science, the inside–outside algorithm is a way of re-estimating production probabilities in a probabilistic context-free grammar.
It was introduced James K. Baker in 1979 as a generalization of the forward–backward algorithm for parameter estimation on hidden Markov models to stochastic context-free grammars. It is used to compute expectations, for example as part of the expectation–maximization algorithm (an unsupervised learning algorithm). Inside and outside probabilities[edit]
File:EM-Gaussian-data.svg. From Wikimedia Commons, the free media repository Summary[edit] Licensing[edit] File history Click on a date/time to view the file as it appeared at that time.
You cannot overwrite this file. File usage on other wikis The following other wikis use this file: Fuzzy clustering. Fuzzy clustering is a class of algorithms for cluster analysis in which the allocation of data points to clusters is not "hard" (all-or-nothing) but "fuzzy" in the same sense as fuzzy logic.
Explanation of clustering[edit] Data clustering is the process of dividing data elements into classes or clusters so that items in the same class are as similar as possible, and items in different classes are as dissimilar as possible. Depending on the nature of the data and the purpose for which clustering is being used, different measures of similarity may be used to place items into classes, where the similarity measure controls how the clusters are formed.
Some examples of measures that can be used as in clustering include distance, connectivity, and intensity. In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. Cluster analysis. The result of a cluster analysis shown as the coloring of the squares into three clusters.
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς "grape") and typological analysis.
Expectation–maximization algorithm. In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables.
