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Unit IV Competitive learning Neural Network

Overview

This unit delves into competitive learning neural networks. It begins with the components of competitive learning networks and explores pattern clustering and feature mapping networks. You'll learn about Adaptive Resonance Theory (ART) networks, their features, and applications such as character recognition using ART networks. The unit also covers Self-Organizing Maps (SOM), detailing two basic feature mapping models, the SOM algorithm, and the properties of feature maps. Computer simulations, learning vector quantization, and adaptive pattern classification are also discussed in this unit.

Topics

  1. Components of Competitive Learning (CL) NetworkComponents of Competitive Learning (CL) NetworkDefinition Competitive Learning (CL) Network is a type of artificial neural network where neurons compete to become active during the learning process. Only the neuron with the highest activation (winner) is updated, reinforcing its weight vectors to match the input pattern. This learning strategy is primarily used for tasks like clustering, pattern recognition, and vector quantisation. Key Concepts Competition*: Neurons in the **network compete to be the one to respond to a given input. Winn
  2. Pattern Clustering and Feature Mapping NetworkPattern Clustering and Feature Mapping NetworkDefinition Pattern Clustering and Feature Mapping Network, also known as Self-Organizing Map (SOM), is a type of Unsupervised Learning network that performs clustering and feature mapping. Developed by Teuvo Kohonen, SOM organizes high-dimensional data into a low-dimensional (typically 2D) grid, preserving the topological properties of the input space. Key Concepts Self-Organisation*: The network learns to organise itself based on the input patterns without supervision. Topology Preservation*
  3. ART NetworksART NetworksDefinition Adaptive Resonance Theory (ART) Networks are a type of neural network developed by Stephen Grossberg that is designed to solve the stability-plasticity dilemma, which refers to the challenge of learning new information without forgetting previously learned information. ART networks are capable of incremental learning, pattern recognition, and clustering in a stable and consistent manner. Key Concepts Stability-Plasticity Dilemma: The balance between **learning new patterns (plastic
  4. Features of ART ModelsFeatures of ART ModelsDefinition Adaptive Resonance Theory (ART) models are a class of neural networks designed to perform pattern recognition and clustering while addressing the stability-plasticity dilemma. They ensure that new information can be learned without erasing previously stored information, making them suitable for real-time and incremental learning tasks. Key Concepts Stability-Plasticity Dilemma*: The balance between retaining existing memories (stability) and learning new patterns (plasticity). Reso
  5. Character Recognition Using ART NetworkCharacter Recognition Using ART NetworkDefinition Character Recognition using an Adaptive Resonance Theory (ART) Network involves the application of ART models to the task of identifying and classifying handwritten or printed characters. ART networks are particularly suited for this task due to their ability to learn incrementally, handle noisy data, and maintain stability in learning new patterns without forgetting previously learned ones. Key Concepts Incremental Learning*: ART networks can learn new character patterns one by on
  6. Self-Organisation Maps (SOM)Self-Organisation Maps (SOM)Definition Self-Organising Maps (SOM), also known as Kohonen maps, are a type of artificial neural network introduced by Teuvo Kohonen. SOMs are used for unsupervised learning and are particularly effective for dimensionality reduction, clustering, and visualization of high-dimensional data by mapping it onto a low-dimensional (usually 2D) grid while preserving the topological relationships of the data. Key Concepts Unsupervised Learning**: SOMs learn to organize and cluster data without labe
  7. Two Basic Feature Mapping ModelsTwo Basic Feature Mapping ModelsDefinition Feature Mapping models are neural network techniques designed to map high-dimensional data onto a lower-dimensional space while preserving the relationships and structures inherent in the data. The two basic feature mapping models are Self-Organizing Maps (SOM) and Adaptive Resonance Theory (ART). Both models aim to organize and cluster data but differ in their learning algorithms and applications. Key Concepts Dimensionality Reduction**: Reducing the number of variables under cons
  8. SOM AlgorithmSOM AlgorithmDefinition The Self-Organizing Map (SOM) algorithm, developed by Teuvo Kohonen, is an unsupervised learning algorithm that produces a low-dimensional (typically 2D) representation of a higher-dimensional input space. It is used for clustering, visualisation, and dimensionality reduction, preserving the topological properties of the input data. Key Concepts Unsupervised Learning**: The SOM algorithm learns to classify data without the need for labeled inputs. Topology Preservation*: The algori
  9. Properties of Feature MapProperties of Feature MapDefinition Feature maps, as used in Self-Organizing Maps (SOM), refer to the grid of neurons that represent the high-dimensional input data in a lower-dimensional space. These maps preserve the topological properties of the input space, allowing similar input patterns to be mapped to nearby neurons. The properties of the feature map are crucial for understanding how SOMs perform clustering, visualization, and dimensionality reduction. Key Concepts Topology Preservation**: Maintaining the spat
  10. Computer SimulationsComputer SimulationsDefinition Computer simulations are the use of computational models to replicate and study the behavior of complex systems and processes. These simulations allow researchers and practitioners to analyze, predict, and visualize outcomes without the need for physical experimentation, saving time and resources while providing deeper insights into the systems being studied. Key Concepts Modelling*: The process of creating a mathematical representation of a real-world system. Simulation*: The exec
  11. Learning Vector QuantisationLearning Vector QuantisationDefinition Learning Vector Quantisation (LVQ) is a supervised learning algorithm that uses prototype vectors to classify input data into predefined categories. It is based on competitive learning, where the prototypes (representing different classes) are updated during training to better represent the underlying data distribution. Key Concepts Prototype Vectors*: Representative vectors for each class that are adjusted during training. Supervised Learning**: LVQ uses labeled input data to lear
  12. Adaptive Pattern ClassificationAdaptive Pattern ClassificationDefinition Adaptive Pattern Classification refers to a class of machine learning algorithms that dynamically adjust their parameters and structure to improve classification performance over time. These algorithms can learn from new data, adapt to changes in data distribution, and improve their ability to recognise and classify patterns accurately. Key Concepts Adaptability*: The ability to adjust to new data and evolving patterns in real-time. Pattern Recognition*: Identifying and classifying

Additional Resources

  • Books:
  • Research Papers:
  • Online Courses:
    • Coursera: "Computational NeuroscienceComputational NeuroscienceComputational Networking Pre-requisites Syllabus Topics that we will cover in this course: 1. Basic Neurobiology 1. Neural Encoding 1. Neural Decoding 1. Information Theory 1. Modeling Single Neurons 1. Synapse and Network Models: Feedforward and Recurrent Networks 1. Synaptic Plasticity and Learning Schedule Week 1*: Course Introduction and Basic Neurobiology (Rajesh Rao*) Week 2*: What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall*) Week 3*: Extracting Information from Neur" by the University of Washington.
  • YouTube Videos:
    • "Hopfield Networks Explained" by NPTEL.
  • Articles and Blogs:
    • Articles on associative memory and Hopfield networks on Medium and Towards Data Science.

Summary

  • High-level summary of the unit.

Questions

  • Draw and explain Competitive learning Network.Draw and explain Competitive learning Network.Competitive Learning Neural Networks Competitive Learning Neural Networks are a type of artificial neural network where neurons compete with each other to be activated. This competition is based on the input data, and only the neuron with the highest activation is updated, which is known as the "winner-takes-all" mechanism. Structure of a Competitive Learning Network 1. Input Layer: The input layer consists of neurons that take in the input features. Each input neuron is connected to each neu
  • Describe the self-organization map (SOM) algorithm and explain how it can be used for feature mapping.Describe the self-organization map (SOM) algorithm and explain how it can be used for feature mapping.Self-Organizing Map (SOM) Algorithm Self-Organizing Maps (SOMs), also known as Kohonen maps, are a type of artificial neural network used for Unsupervised Learning. They are particularly useful for dimensionality reduction, clustering, and feature mapping. The main idea behind SOMs is to produce a low-dimensional (typically 2D), discretised representation of the input space of the training samples. Structure of a Self-Organising Map 1. Grid of Neurons: SOMs consist of neurons arranged in a gr
  • Explain how ART can be used for character recognition task.Explain how ART can be used for character recognition task.Adaptive Resonance Theory (ART) and Character Recognition Adaptive Resonance Theory (ART) is a neural network architecture developed by Stephen Grossberg and Gail Carpenter in the late 1980s. It is designed for pattern recognition tasks, particularly useful for character recognition, due to its ability to perform stable learning without forgetting previously learned patterns. ART is well-suited for tasks that require incremental learning and adaptation to new data. Key Concepts of ART 1. Stab
  • Explain briefly ART network. What are the features of ART network?
  • Describe the components of a competitive learning neural network and explain how they contribute to the network function.Describe the components of a competitive learning neural network and explain how they contribute to the network function.Components of a Competitive Learning Neural Network Competitive Learning Neural Networks are designed to perform clustering and pattern recognition by allowing neurons to compete for activation. The key components of these networks include the input layer, competitive layer (output layer), and the learning mechanism. Each of these components plays a critical role in the network's functioning. 1. Input Layer Function: * The input layer receives the input data or features to be processed by th
  • What is vector quantization? How it is used for pattern clustering?
  • What is competitive learning in neural networks?
  • Consider an ART-I network with input vector...?
  • Draw the network architecture of ART network. Explain the algorithm for designing the weights of ART network.Draw the network architecture of ART network. Explain the algorithm for designing the weights of ART network.Network Architecture of the ART (Adaptive Resonance Theory) Network The ART network consists of two primary layers: the Comparison Layer (F1) and the Recognition Layer (F2), connected by bottom-up and top-down weights. Below is a diagram representing the basic architecture of an ART network: +---------------------+ | F2 Layer | | (Recognition Layer) | +---------+-----------+ | Bottom-Up | Top-Down
  • Explain ART under the following headings 1) Architecture 2) Working 3) Training 4) ImplementationExplain ART under the following headings 1) Architecture 2) Working 3) Training 4) ImplementationAdaptive Resonance Theory (ART) Adaptive Resonance Theory (ART) is a family of neural network models designed to perform pattern recognition and unsupervised learning. ART networks are particularly known for their ability to perform stable learning of new patterns without forgetting previously learned information. Below, ART is explained under the specified headings. 1) Architecture The architecture of an ART network consists of the following key components: Input Layer**: The input layer re
  • Draw the architecture of the Köhonen Network and explain the algorithm for training the weights of the Network.Draw the architecture of the Köhonen Network and explain the algorithm for training the weights of the Network.Architecture of the Kohonen Network (Self-Organizing Map, SOM) The Kohonen Network, also known as a Self-Organizing Map (SOM), is a type of competitive learning neural network used for unsupervised learning tasks such as clustering and dimensionality reduction. The key idea behind SOM is to map high-dimensional input data into a lower-dimensional (typically two-dimensional) grid of neurons while preserving the topological properties of the input data. Architecture Diagram Input Laye
  • Define following. 1) Learning vector quantisation ii) Adaptive pattern classificationDefine following. 1) Learning vector quantisation ii) Adaptive pattern classificationLearning Vector Quantisation (LVQ) Definition: Learning Vector Quantisation (LVQ) is a supervised learning algorithm used for classification tasks. It is a type of competitive learning neural network that relies on the concept of prototypes, which are representative vectors for each class. LVQ is designed to improve the accuracy of vector quantization by leveraging labeled training data. Key Concepts: 1. Prototypes: * Prototypes are representative vectors for each class in the dataset.

Note-taking and Annotation Strategy

  • Case Studies: Document case studies of associative learning applications.
  • Simulations: Run simulations of Hopfield networks and Boltzmann machines, noting observations.
  • Concept Maps: Map out key concepts and their relationships.