Structured Learning Theory

Scandura, J.M., Instructional Strategies Based on the Structural Learning Theory, in C.M. Reigeluth (ed). Instructional Theories in Action Lessons Illustrating Selected Theories and Models, Lawrence Erlbaum Associates, Hillsdale, NJ, p.213-246,1987. 

In conclusion, structured programs that are based in social learning theory, as well as conjoint treatment, are nonmedical interventions that seem to enhance outcomes in the treatment of drug-use disorders. Our earlier discussion showed that treatment of alcohol-use disorders also is improved with these interventions. This seems to warrant their continued application and study in alcohol and drug treatment programs. 

Through a comparison of the insecticidal and toxicological data, structure-activity theories were developed which provided a means for the synthesis of safer compounds. A similar approach was used in the 2,4-D area. All manner of substituted phenoxy and benzoic acids and their derivatives were prepared. As a result, much was learned about the structural relationships for the auxin type action. This analog synthesis procedure has often been called "me too chemistry". The patent literature abounds with examples of such a strategy tried on almost everything that has shown a modicum of biological activity. 

The learning theory of Conceptual Growth becomes quite clear at this point students have obviously observed that temperatures of things increase when thermal energy is added - the existing cognitive structure supplies the basis of the mentioned preconcepts. 

A. Sagues, R. G. Powers, Corrosion and corrosion control of concrete structures in Florida. What can be learned , Proc. Int. Conf. Repair of Concrete Structures. From Theory to Practice in a Marine Environment, Svolvear (Norway), 28-30 May 1997, p. 49. 

Support vector machine (SVM) is originally a binary supervised classification algorithm, introduced by Vapnik and his co-workers [13, 32], based on statistical learning theory. Instead of traditional empirical risk minimization (ERM), as performed by artificial neural network, SVM algorithm is based on the structural risk minimization (SRM) principle. In its simplest form, linear SVM for a two class problem finds an optimal hyperplane that maximizes the separation between the two classes. The optimal separating hyperplane can be obtained by solving the following quadratic optimization problem ... 

The methods of discrete mathematics, introduced in this chapter, were sufficient for describing chemical compounds as discrete structures. However, once the relationships between properties and structures have to be modelled, non-discrete methods are required. Methods from supervised statistical learning theory and machine learning are particularly useful and thus some of these will be introduced in the next chapter. 

Neuronal networks are nowadays predominantly applied in classification tasks. Here, three kind of networks are tested First the backpropagation network is used, due to the fact that it is the most robust and common network. The other two networks which are considered within this study have special adapted architectures for classification tasks. The Learning Vector Quantization (LVQ) Network consists of a neuronal structure that represents the LVQ learning strategy. The Fuzzy Adaptive Resonance Theory (Fuzzy-ART) network is a sophisticated network with a very complex structure but a high performance on classification tasks. Overviews on this extensive subject are given in [2] and [6]. 

A common use of statistics in structural biology is as a tool for deriving predictive distributions of strucmral parameters based on sequence. The simplest of these are predictions of secondary structure and side-chain surface accessibility. Various algorithms that can learn from data and then make predictions have been used to predict secondary structure and surface accessibility, including ordinary statistics [79], infonnation theory [80], neural networks [81-86], and Bayesian methods [87-89]. A disadvantage of some neural network methods is that the parameters of the network sometimes have no physical meaning and are difficult to interpret. 

Most materials scientists at an early stage in their university courses learn some elementary aspects of what is still miscalled strength of materials . This field incorporates elementary treatments of problems such as the elastic response of beams to continuous or localised loading, the distribution of torque across a shaft under torsion, or the elastic stresses in the components of a simple girder. Materials come into it only insofar as the specific elastic properties of a particular metal or timber determine the numerical values for some of the symbols in the algebraic treatment. This kind of simple theory is an example of continuum mechanics, and its derivation does not require any knowledge of the crystal structure or crystal properties of simple materials or of the microstructure of more complex materials. The specific aim is to design simple structures that will not exceed their elastic limit under load. 

Colloidal crystals . At the end of Section 2.1.4, there is a brief account of regular, crystal-like structures formed spontaneously by two differently sized populations of hard (polymeric) spheres, typically near 0.5 nm in diameter, depositing out of a colloidal solution. Binary superlattices of composition AB2 and ABn are found. Experiment has allowed phase diagrams to be constructed, showing the crystal structures formed for a fixed radius ratio of the two populations but for variable volume fractions in solution of the two populations, and a computer simulation (Eldridge et al. 1995) has been used to examine how nearly theory and experiment match up. The agreement is not bad, but there are some unexpected differences from which lessons were learned. 

While the conditional gene knockout experiments are supportive of a role for the NMDA receptors in memory, they are less than fully conclusive in linking the synaptic coincidence-detection feature of the NMDA receptor to memory formation. Like all loss-of-function studies, CA1-specific gene-knockout experiments could, in theory, produce memory impairment via a mechanism independent of the coincidence-detection function of the NMDA receptor. For example, one may argue that the physical absence of the NMDA receptor channels may cause subtle structural reconfiguration at the synapse, thereby altering normal synaptic transmission. Therefore, the memory impairment in CA1-specific NR1 knockout mice does not allow a firm conclusion that the coincidence-detection function of NMDA receptors controls learning and memory processes at the cellular level. 

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