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An important feature of NNs is their ability to learn from their environment 3 covered NN types that learned under the guidance of a supervisor or teacher The supervisor presents the NN learner with an input pattern and a desired response Supervised learning NNs then try to learn the functional mapping between the input and desired response vectors In contrast to supervised learning, the objective of unsupervised learning is to discover patterns or features in the input data with no help from a teacher This chapter deals with the unsupervised learning paradigm Section 41 presents a short background on unsupervised learning Hebbian learning is presented in Section 42, while Section 43 covers principal component learning, Section 44 covers the learning vector quantizer version I, and Section 45 discusses self-organizing feature maps

In fact, the Message Store is an indexed collection of les designed for rapid access You should not directly alter the Message Store unless you know what you are doing, as you will almost certainly corrupt one or more messages or even the whole store However, it can be useful for debugging purposes to take a complete copy of the Message Store, particularly with the emulator The data in message headers and bodies is accessed by means of APIs that expose the structure of the message However, internally message headers and bodies are contained in stores in the les of the message entries A store is a le containing one or more streams that are written to or read from by some software Because the format and contents vary between message types, the MTMs are responsible for writing the messages into stores and for reading them out again.

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Aristotle observed that human memory has the ability to connect items (eg objects, feelings and ideas) that are similar, contradictory, that occur in close proximity, or in succession [473] The patterns that we associate may be of the same or di erent types For example, a photo of the sea may bring associated thoughts of happiness, or smelling a speci c fragrance may be associated with a certain feeling, memory or visual image Also, the ability to reproduce the pitch corresponding to a note, irrespective of the form of the note, is an example of the pattern association behavior of the human brain Arti cial neural networks have been developed to model the pattern association ability of the human brain These networks are referred to as associative memory NNs Associative memory NNs are usually two-layer NNs, where the objective is to adjust the weights such that the network can store a set of pattern associations without any external help from a teacher The development of these associative memory NNs is mainly inspired from studies of the visual and auditory cortex of mammalian organisms, such as the bat These arti cial NNs are based on the fact that parts of the

Computational Intelligence: An Introduction, Second Edition AP Engelbrecht c 2007 John Wiley & Sons, Ltd

(221)

Unless you are implementing an MTM, you will not directly access the store for a message entry When required, the store for a message entry is available by means of the CMsvEntry::ReadStoreL() and CMsvEntry::EditStoreL() methods The body text can then be extracted using the CMsvStore::RestoreBodyTextL() method and stored using the CMsvStore::StoreBodyTextL() method The data contained in TMsvEntry and CMsvEntry objects is not written to the Message Store whenever changes are made This would be inef cient, particularly if a number of changes are being made to an object Instead, these objects act as a local cache and the developer is responsible for writing the changes to the Message Store (normally when all changes have been made) For SMS messages, the CSmsClientMtm::LoadMessageL() method reads message data from the Message Store into objects, and the CSmsClientMtm::SaveMessageL() method writes changes back to the Message Store..

brain are organized such that di erent sensory inputs are represented by topologically ordered computational maps The networks form a topographic map of the input patterns, where the coordinates of the neurons correspond to intrinsic features of the input patterns An additional feature modeled with associative memory NNs is to preserve old information as new information becomes available In contrast, supervised learning NNs have to retrain on all the information when new data becomes available; if not, supervised networks tend to focus on the new information, forgetting what the network has already learned Unsupervised learning NNs are functions that map an input pattern to an associated target pattern, ie (41) fN N : RI RK as illustrated in Figure 41 The single weight matrix determines the mapping from the input vector z to the output vector o

The Hebbian learning rule, named after the neuropsychologist Hebb, is the oldest and simplest learning rule With Hebbian learning [203], weight values are adjusted based on the correlation of neuron activation values The motivation of this approach is from Hebb s hypothesis that the ability of a neuron to re is based on that neuron s ability to cause other neurons connected to it to re In such cases the weight between the two correlated neurons is strengthened (or increased) Using the notation from

It may be noted that by using the coordinate frame a, b de ned by the axes of the ellipse, the resolved disturbances have a phase difference of /2, and (221) reduces

42 Hebbian Learning Rule Figure 41, the change in weight at time step t is given as uki (t) = ok,p zi,p Weights are then updated using uki (t) = uki (t 1) + uki (t) where is the learning rate

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