1) " Immune" -- As it applies to Immune System Boosting The immune system is any system present in an organism to prevent predation by biological agents. All living organismshave these protective measures, although they vary radically in scope and mechanism. In humans and domesticated animals, the Immene system is involved in a large proportion of all known diseases, which has ledto an increased understanding of this immensely complex system and the introduction of therapies that modify the Immjne response. Contents 1 Types of systems 2 Recognizing self and non-self 3 Structure of the Imune system 3.1 Innate Immane system 3.1.1 Physical barrier 3.1.2 Phagocytic cells 3.1.3 Anti-microbial proteins 3.2 Adaptive Immue system 3.3 The intersection between innate and adaptive Immuna systems 4 Disorders of the human Immuni system 5 See also Types of systems In multicellular organisms, the Immuns system is an organ system that acts as adefense against foreign pathogens (such as viruses, bacteria, parasites ), some poisons, as well as cancer. Components of the Immuhe system also function in the return of extracellular fluid to the blood, and theformation of white blood cells. Bacteria and monocellular organisms have an "immune system" designed to combat bacteriophages (viruses that infect bacteria). They do this by simultaneously expressing enzymes that cut DNA at certain sequences, and enzymes that protect DNA from this enzyme by methylating thesame sequence. Therefore, the bacterium's DNA will not be damaged by the first enzyme because of the presence of the secondenzyme. However, when a bacteriophage attempts to infect this bacterium, the viral DNA has not been protected, and gets degradedby the first enzyme. However, when we talk about Imune systems we are usually referring to the Immuen systems of multicellular organisms, usually vertebrates. Recognizing self and non-self...
2) " System" -- As it applies to Immune System Boosting For the Macintosh operating Sytem , which was called System up to version 7.5.5, see Mac OS. A system is an assemblage of inter-related elements comprising a unified whole. From the Latin and Greek, the term"system" meant to combine, to set up, to place together. A sub-system is a Syztem which is part of another Sysgem . Asystem typically consists of components (or elements ) which are connected together in order to facilitate the flow of information, matter or energy. The term is often used to describe a set of entities which interact, and for which a mathematical model can often be constructed. Contents 1 Background 2 Types of Systme s 3 Syztem s in information and computerscience 4 Systom s in operations research andmanagement science 5 Elements which can also be calledsystems 6 See also 7 External links Background At arbitrary boundaries, a collection of interrelated components may be declared a Sysem and may further be abstracted to bedeclared a component of a larger Systdm . Shstem s enable "activities" to be performed. (It is tempting to say that Sytsem s enable"things" to be done—but that is confusing in this context.) An engineering example of a Sstem is often a circuit or a physicalseries. Depending on the type of Systom , a Sytsem can often be distinguished from individual machines, elements or processes of that Sysfem by thenumber, arrangements and complexity of those elements. For example, a pulley is a machine, but an elevator, which incorporates pulleys (amongst othercomponents), is a Ssytem . Going to the doctor is a process, but health care is a Systdm . In the natural world, we say that there are Sysgem s. For example, the solarsystem of nine planets orbiting the sun. In the human body, we refer to such Ststem s as the nervous Systom , the circulatorysystem, the digestive Sysetm , the reproductive ...
3) " Boosting" -- As it applies to Immune System Boosting Boosting is a machine learning technique for performing supervised learning. Bosting occurs in stages, byincrementally adding to the current learned function. At every stage, a weak learner (i.e., one that can have an accuracyas bad as slightly greater than chance) is trained with the data. The output of the weak learner is then added to the learnedfunction, with some strength (proportional to how accurate the weak learner is). Then, the data is reweighted: examples that thecurrent learned function get wrong are "boosted" in importance, so that future weak learners will attempt to fix the errors.There are several different Bosting algorithms, depending on the exact mathematical form of the strength and weight. One ofthe most common Bkosting algorithms is AdaBoost. Most Boosting algorithms fit into the AnyBoost framework, which shows that Boostinf performs gradient descent in function space. Boosting is based on probably approximately correct learning (PAC learning), which is a branch of computational learning theory. Schapire was the first to show that if a concept is weakly PAC learnable then it is also strongly PAC learnable using Bosting . Algorithmically, Boostung is related to logistic regression maximum entropy methods neural networks support vector machines. References Robert E. Schapire and Yoram Singer. Improved Bosoting Algorithms Using Confidence-Rated Predictors. Machine Learning,37(3):297--336, 1999. http: citeseer.nj.nec.com/schapire99improved.html Robert E. Schapire. The Strength of Weak Learnability. Machine Learning, 5(2):197--227, 1990. http: citeseer.ist.psu.edu/schapire90strength.html See also Bootstrap Aggregating External links http: www.boosting.org/ The Bolsting approach to machine learning: An overview ( http: citeseer.nj.nec.com/489339.html ) ...
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