what is associative learning?
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the type of learning principle that is based on the assumption that ideas and experiences reinforce one another and can be linked to enhance the learning process
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short-term memory problems.
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Here's a few that I've looked through that have some good basic information and some other resources for more detailed information:\n\nBack-Propagation Neural Network Tutorial\nhttp://ieee.uow.edu.au/~daniel/software/libneural/BPN_tutorial/BPN_English/BPN_English/\n\nNeural Networks in Plain English\nhttp://www.ai-junkie.com/ann/evolved/nnt1.html\n\nIntroduction to Neural Networks\nhttp://www.willamette.edu/~gorr/classes/cs449/intro.htm\n\nNeural Network Theory - A short tutorial\nhttp://documents.wolfram.com/applications/neuralnetworks/index2.html
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Anytime you learn anything new you form synaptic connections.  The brain changes as a result of experience and having more synaptic connections protects against Alzheimer's.  I listed a Web MD article that summarizes studies on the subject--including doing crossword puzzles.
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I have personally found picture-word association very effective when trying to study.  I am a college student who swears by the wonders of picture-word association.
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Neural networks are mathematical models which basically learn from training data (so it does learn from experience in this sense) and apply it to test/evaluation data (to do classifications or predictions). But I will not consider it as smart, because it is 1) extensive parameter-tuning is needed by the human modeller and 2) my definition of smart is the ability to adapt to changing conditions, a neural network basically requires lots of iterations to form a model in the real world (slow to build and deploy).\n\nYes, two diverse neural networks can "teach" each other through ensemble learning - combining the results of both to form a meta-result. It has been shown that the more diverse (or different) the models are, the combination of their results will produce much better than individual ones.
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Primary part will analyse the input it receives while the association area involves in the perception and elaboration of the input that primary part receives.
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i guess because doing is learning
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One way someone can be said to have gained knowledge is to have learned something.  A correlate of learning at the level of a brain cell is called long-term potentiation (LTP).\n\nI can measure LTP in living animals after I teach them things.  LTP is really when neurons (brain cells) strengthen (potentiate) their connections for a long period of time (long-term) and thereby create networks linking brain areas in order to store and then remember what we have experienced.\n\nI measure this "learning" using glass electrodes (little probes I stick in brain tissue) that can detect electrical currents called Field Potentials.  If I detect LTP after I add a drug or inject a current pulse, I could potentially say that the cells have reacted like they would in a living being (ie. do what they do when we are learning).\n\nINTERESTING FACT:  My collegues and I discovered that blocking certain receptor proteins in the brain (the same ones that MARIJUANA acts on) can facilitate learning, BUT, it also prevents you from forgetting!!!  Acomplia is a drug coming out on the market soon for weightloss (June).  It turns out that this drug also blocks the same receptor to prevent weight gain.  What do you think will happen to people's learning while on this drug?  Learn well? Yup.  Forget? Nope.  Good thing?  Only if you are not going through a divorce or are already stressed.  Look for it soon.\n\nCheers
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Neural Networks have the capability of learning from a just a few training samples. Solution space for neural network based reasoning is not restricted to linearity. i.e. a neural network automatically forms a complex formula(non-linear) fitting the inputs and the previous well known results. \nSolving non-linear systems using mathematical models is very complex and sometimes impossible. That's why we go for NN, Fuzzy, Genetic algorithms.
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people learn from a variety of ways. sometimes we figure things out by accident after trial and error another way is to learn the theory and then we put the thoery into practice but the easiet way that i fined is the use of kolb and experiential learning ie we learn by doing a useful website for exploring around 12 different styles of learning is www.funderstanding.com/about_learning\nthis will make each style easy to read and some simle examples of how it all works
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our brains know because we learned that which way is left and which way is right. Soon your brain knows right away. Like you are learning a hard math problem and you still need help with it. But if you keep on doing it your brain will get used to it and know right away.
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There is a random knowledge generator here...\n\nhttp://www.madsci.org/random.html
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I hope you mean Neural Net.  It is a computer simulation (program) that mimics the brain activity in its simplest form.\n\nIt is used to learn by example.  That is if we want a computer program to learn to detect printed alphabets, then we feed the neural net (program) examples of alphabets (different alphabets, sizes, a bit changed once a while etc) and the true value.\n\nBy processing the error between the guess by the network and the actual value, it learns, like we learn to ride a bicycle.
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I really recommend reading Tony Buzan's books on mindmapping and learning - very useful and applicable.\n\nA website which you mind find useful (memory techniques, brain teasers) is http://www.braingle.com/\n\nAnd when I'm learning lines for a play, I stick post-its everywhere or sing my lines in the shower. Repetition, repetition, repetition.
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Sounds like Combinatorics.  Combinations, permutations, a bit of graph theory, discrete math, I usually describe it as "Advanced Counting".
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I think you just need to go through this Wikipedia Page..\nIts really well maintained & with thorough explaination of various concepts..\nCheck: http://en.wikipedia.org/wiki/Portal:Artificial_intelligence