Wikipedia - User contributions [en]

<?xml version="1.0"?> <feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"> <id>https://en.wikipedia.org/w/api.php?action=feedcontributions&feedformat=atom&user=Dodgyb</id> <title>Wikipedia - User contributions [en]</title> <link rel="self" type="application/atom+xml" href="https://en.wikipedia.org/w/api.php?action=feedcontributions&feedformat=atom&user=Dodgyb"/> <link rel="alternate" type="text/html" href="https://en.wikipedia.org/wiki/Special:Contributions/Dodgyb"/> <updated>2024-11-30T17:01:03Z</updated> <subtitle>User contributions</subtitle> <generator>MediaWiki 1.44.0-wmf.5</generator> <entry> <id>https://en.wikipedia.org/w/index.php?title=User:Dodgyb/Books/Machine_Learning&diff=618805464</id> <title>User:Dodgyb/Books/Machine Learning</title> <link rel="alternate" type="text/html" href="https://en.wikipedia.org/w/index.php?title=User:Dodgyb/Books/Machine_Learning&diff=618805464"/> <updated>2014-07-28T11:42:12Z</updated> <summary type="html">Dodgyb: 鈫怌reated page with '{{saved book |title= |subtitle= |cover-image= |cover-color=}} == Machine Learning == ;Introduction and Main Principles :Machine learning :Data analysi...' <hr /> <div>{{saved book |title= |subtitle= |cover-image= |cover-color=}} == Machine Learning == ;Introduction and Main Principles :[[Machine learning]] :[[Data analysis]] :[[Occam's razor]] :[[Curse of dimensionality]] :[[No free lunch theorem]] :[[Accuracy paradox]] :[[Overfitting]] :[[Regularization (machine learning)]] :[[Inductive bias]] :[[Data dredging]] :[[Ugly duckling theorem]] :[[Uncertain data]] ;Background and Preliminaries ;Knowledge discovery in Databases :[[Knowledge discovery]] :[[Data mining]] :[[Predictive analytics]] :[[Predictive modelling]] :[[Business intelligence]] :[[Reactive business intelligence]] :[[Business analytics]] :[[Reactive business intelligence]] :[[Pattern recognition]] ;Reasoning :[[Abductive reasoning]] :[[Inductive reasoning]] :[[First-order logic]] :[[Inductive logic programming]] :[[Reasoning system]] :[[Case-based reasoning]] :[[Textual case based reasoning]] :[[Causality]] ;Search Methods :[[Nearest neighbor search]] :[[Stochastic gradient descent]] :[[Beam search]] :[[Best-first search]] :[[Breadth-first search]] :[[Hill climbing]] :[[Grid search]] :[[Brute-force search]] :[[Depth-first search]] :[[Tabu search]] :[[Anytime algorithm]] ;Statistics :[[Exploratory data analysis]] :[[Covariate]] :[[Statistical inference]] :[[Algorithmic inference]] :[[Bayesian inference]] :[[Base rate]] :[[Bias (statistics)]] :[[Gibbs sampling]] :[[Cross-entropy method]] :[[Latent variable]] :[[Maximum likelihood]] :[[Maximum a posteriori estimation]] :[[Expectation鈥搈aximization algorithm]] :[[Expectation propagation]] :[[Kullback鈥揕eibler divergence]] :[[Generative model]] ;Main Learning Paradigms :[[Supervised learning]] :[[Unsupervised learning]] :[[Active learning (machine learning)]] :[[Reinforcement learning]] :[[Multi-task learning]] :[[Transduction (machine learning)|Transduction]] :[[Explanation-based learning]] :[[Offline learning]] :[[Online learning model]] :[[Online machine learning]] :[[Hyperparameter optimization]] ;Classification Tasks :[[Classification in machine learning]] :[[Concept class]] :[[Features (pattern recognition)]] :[[Feature vector]] :[[Feature space]] :[[Concept learning]] :[[Binary classification]] :[[Decision boundary]] :[[Multiclass classification]] :[[Class membership probabilities]] :[[Calibration (statistics)]] :[[Concept drift]] :[[Prior knowledge for pattern recognition]] ;Online Learning :[[Margin Infused Relaxed Algorithm]] ;Semi-supervised learning :[[Semi-supervised learning]] :[[One-class classification]] :[[Coupled pattern learner]] ;Lazy learning and nearest neighbors :[[Lazy learning]] :[[Eager learning]] :[[Instance-based learning]] :[[Cluster assumption]] :[[K-nearest neighbor algorithm]] :[[IDistance]] :[[Large margin nearest neighbor]] ;Decision Trees :[[Decision tree learning]] :[[Decision stump]] :[[Pruning (decision trees)]] :[[Mutual information]] :[[Adjusted mutual information]] :[[Information gain ratio]] :[[Information gain in decision trees]] :[[ID3 algorithm]] :[[C4.5 algorithm]] :[[CHAID]] :[[Information Fuzzy Networks]] :[[Grafting (decision trees)]] :[[Incremental decision tree]] :[[Alternating decision tree]] :[[Logistic model tree]] :[[Random forest]] ;Linear Classifiers :[[Linear classifier]] :[[Margin (machine learning)]] :[[Margin classifier]] :[[Soft independent modelling of class analogies]] ;Statistical classification :[[Statistical classification]] :[[Probability matching]] :[[Discriminative model]] :[[Linear discriminant analysis]] :[[Multiclass LDA]] :[[Multiple discriminant analysis]] :[[Optimal discriminant analysis]] :[[Fisher kernel]] :[[Discriminant function analysis]] :[[Multilinear subspace learning]] :[[Quadratic classifier]] :[[Variable kernel density estimation]] :[[Category utility]] ;Evaluation of Classification Models :[[Data classification (business intelligence)]] :[[Training set]] :[[Test set]] :[[Synthetic data]] :[[Cross-validation (statistics)]] :[[Loss function]] :[[Hinge loss]] :[[Generalization error]] :[[Type I and type II errors]] :[[Sensitivity and specificity]] :[[Precision and recall]] :[[F1 score]] :[[Confusion matrix]] :[[Matthews correlation coefficient]] :[[Receiver operating characteristic]] :[[Lift (data mining)]] :[[Stability in learning]] ;Features Selection and Features Extraction :[[Data Pre-processing]] :[[Discretization of continuous features]] :[[Feature selection]] :[[Feature extraction]] :[[Dimension reduction]] :[[Principal component analysis]] :[[Multilinear principal-component analysis]] :[[Multifactor dimensionality reduction]] :[[Targeted projection pursuit]] :[[Multidimensional scaling]] :[[Nonlinear dimensionality reduction]] :[[Kernel principal component analysis]] :[[Kernel eigenvoice]] :[[Gramian matrix]] :[[Gaussian process]] :[[Kernel adaptive filter]] :[[Isomap]] :[[Manifold alignment]] :[[Diffusion map]] :[[Elastic map]] :[[Locality-sensitive hashing]] :[[Spectral clustering]] :[[Minimum redundancy feature selection]] ;Clustering :[[Cluster analysis]] :[[K-means clustering]] :[[K-means++]] :[[K-medians clustering]] :[[K-medoids]] :[[DBSCAN]] :[[Fuzzy clustering]] :[[BIRCH (data clustering)]] :[[Canopy clustering algorithm]] :[[Cluster-weighted modeling]] :[[Clustering high-dimensional data]] :[[Cobweb (clustering)]] :[[Complete-linkage clustering]] :[[Constrained clustering]] :[[Correlation clustering]] :[[CURE data clustering algorithm]] :[[Data stream clustering]] :[[Dendrogram]] :[[Determining the number of clusters in a data set]] :[[FLAME clustering]] :[[Hierarchical clustering]] :[[Information bottleneck method]] :[[Lloyd's algorithm]] :[[Nearest-neighbor chain algorithm]] :[[Neighbor joining]] :[[OPTICS algorithm]] :[[Pitman鈥揧or process]] :[[Single-linkage clustering]] :[[SUBCLU]] :[[Thresholding (image processing)]] :[[UPGMA]] ;Evaluation of Clustering Methods :[[Rand index]] :[[Dunn index]] :[[Davies鈥揃ouldin index]] :[[Jaccard index]] :[[MinHash]] :[[K q-flats]] ;Rule Induction :[[Decision rules]] :[[Rule induction]] :[[Classification rule]] :[[CN2 algorithm]] :[[Decision list]] :[[First Order Inductive Learner]] ;Association rules and Frequent Item Sets :[[Association rule learning]] :[[Apriori algorithm]] :[[Contrast set learning]] :[[Affinity analysis]] :[[K-optimal pattern discovery]] ;Ensemble Learning :[[Ensemble learning]] :[[Ensemble averaging]] :[[Consensus clustering]] :[[AdaBoost]] :[[Boosting]] :[[Bootstrap aggregating]] :[[BrownBoost]] :[[Cascading classifiers]] :[[Co-training]] :[[CoBoosting]] :[[Gaussian process emulator]] :[[Gradient boosting]] :[[LogitBoost]] :[[LPBoost]] :[[Mixture model]] :[[Product of Experts]] :[[Random multinomial logit]] :[[Random subspace method]] :[[Weighted Majority Algorithm]] :[[Randomized weighted majority algorithm]] ;Graphical Models :[[Graphical model]] :[[State transition network]] ;Bayesian Learning Methods :[[Naive Bayes classifier]] :[[Averaged one-dependence estimators]] :[[Bayesian network]] :[[Bayesian additive regression kernels]] :[[Variational message passing]] ;Markov Models :[[Markov model]] :[[Maximum-entropy Markov model]] :[[Hidden Markov model]] :[[Baum鈥揥elch algorithm]] :[[Forward鈥揵ackward algorithm]] :[[Hierarchical hidden Markov model]] :[[Markov logic network]] :[[Markov chain Monte Carlo]] :[[Markov random field]] :[[Conditional random field]] :[[Predictive state representation]] ;Learning Theory :[[Computational learning theory]] :[[Version space]] :[[Probably approximately correct learning]] :[[Vapnik鈥揅hervonenkis theory]] :[[Shattering (machine learning)]] :[[VC dimension]] :[[Minimum description length]] :[[Bondy's theorem]] :[[Inferential theory of learning]] :[[Rademacher complexity]] :[[Teaching dimension]] :[[Subclass reachability]] :[[Sample exclusion dimension]] :[[Unique negative dimension]] :[[Uniform convergence (combinatorics)]] :[[Witness set]] ;Support Vector Machines :[[Kernel methods]] :[[Support vector machine]] :[[Structural risk minimization]] :[[Empirical risk minimization]] :[[Kernel trick]] :[[Least squares support vector machine]] :[[Relevance vector machine]] :[[Sequential minimal optimization]] :[[Structured SVM]] ;Regression analysis :[[Outline of regression analysis]] :[[Regression analysis]] :[[Dependent and independent variables]] :[[Linear model]] :[[Linear regression]] :[[Least squares]] :[[Linear least squares (mathematics)]] :[[Local regression]] :[[Additive model]] :[[Antecedent variable]] :[[Autocorrelation]] :[[Backfitting algorithm]] :[[Bayesian linear regression]] :[[Bayesian multivariate linear regression]] :[[Binomial regression]] :[[Canonical analysis]] :[[Censored regression model]] :[[Coefficient of determination]] :[[Comparison of general and generalized linear models]] :[[Compressed sensing]] :[[Conditional change model]] :[[Controlling for a variable]] :[[Cross-sectional regression]] :[[Curve fitting]] :[[Deming regression]] :[[Design matrix]] :[[Difference in differences]] :[[Dummy variable (statistics)]] :[[Errors and residuals in statistics]] :[[Errors-in-variables models]] :[[Explained sum of squares]] :[[Explained variation]] :[[First-hitting-time model]] :[[Fixed effects model]] :[[Fraction of variance unexplained]] :[[Frisch鈥揥augh鈥揕ovell theorem]] :[[General linear model]] :[[Generalized additive model]] :[[Generalized additive model for location, scale and shape]] :[[Generalized estimating equation]] :[[Generalized least squares]] :[[Generalized linear array model]] :[[Generalized linear mixed model]] :[[Generalized linear model]] :[[Growth curve]] :[[Guess value]] :[[Hat matrix]] :[[Heckman correction]] :[[Heteroscedasticity-consistent standard errors]] :[[Hosmer鈥揕emeshow test]] :[[Instrumental variable]] :[[Interaction (statistics)]] :[[Isotonic regression]] :[[Iteratively reweighted least squares]] :[[Kitchen sink regression]] :[[Lack-of-fit sum of squares]] :[[Leverage (statistics)]] :[[Limited dependent variable]] :[[Linear probability model]] :[[Mallows's Cp|Mallows's ''C&lt;sub&gt;p&lt;/sub&gt;'']] :[[Mean and predicted response]] :[[Mixed model]] :[[Moderation (statistics)]] :[[Moving least squares]] :[[Multicollinearity]] :[[Multiple correlation]] :[[Multivariate probit]] :[[Multivariate adaptive regression splines]] :[[Newey鈥揥est estimator]] :[[Non-linear least squares]] :[[Nonlinear regression]] ;Logistic Regression :[[Logit]] :[[Multinomial logit]] :[[Logistic regression]] ;Bio-inspired Methods :[[Bio-inspired computing]] ;Evolutionary Algorithms :[[Evolvability (computer science)]] :[[Evolutionary computation]] :[[Evolutionary algorithm]] :[[Genetic algorithm]] :[[Chromosome (genetic algorithm)]] :[[Crossover (genetic algorithm)]] :[[Fitness function]] :[[Evolutionary data mining]] :[[Genetic programming]] :[[Learnable Evolution Model]] ;Neural Networks :[[Neural network]] :[[Artificial neural network]] :[[Artificial neuron]] :[[Types of artificial neural networks]] :[[Perceptron]] :[[Multilayer perceptron]] :[[Activation function]] :[[Self-organizing map]] :[[Attractor network]] :[[ADALINE]] :[[Adaptive Neuro Fuzzy Inference System]] :[[Adaptive resonance theory]] :[[IPO underpricing algorithm]] :[[ALOPEX]] :[[Artificial Intelligence System]] :[[Autoassociative memory]] :[[Autoencoder]] :[[Backpropagation]] :[[Bcpnn]] :[[Bidirectional associative memory]] :[[Biological neural network]] :[[Boltzmann machine]] :[[Restricted Boltzmann machine]] :[[Cellular neural network]] :[[Cerebellar Model Articulation Controller]] :[[Committee machine]] :[[Competitive learning]] :[[Compositional pattern-producing network]] :[[Computational cybernetics]] :[[Computational neurogenetic modeling]] :[[Confabulation (neural networks)]] :[[Cortical column]] :[[Counterpropagation network]] :[[Cover's theorem]] :[[Cultured neuronal network]] :[[Dehaene-Changeux Model]] :[[Delta rule]] :[[Early stopping]] :[[Echo state network]] :[[The Emotion Machine]] :[[Evolutionary Acquisition of Neural Topologies]] :[[Extension neural network]] :[[Feed forward (control)|Feed-forward]] :[[Feedforward neural network]] :[[Generalized Hebbian Algorithm]] :[[Generative topographic map]] :[[Group method of data handling]] :[[Growing self-organizing map]] :[[Memory-prediction framework]] :[[Helmholtz machine]] :[[Hierarchical temporal memory]] :[[Hopfield network]] :[[Hybrid neural network]] :[[HyperNEAT]] :[[Infomax]] :[[Instantaneously trained neural networks]] :[[Interactive Activation and Competition]] :[[Leabra]] :[[Learning Vector Quantization]] :[[Lernmatrix]] :[[Linde鈥揃uzo鈥揋ray algorithm]] :[[Liquid state machine]] :[[Long short term memory]] :[[Madaline]] :[[Modular neural networks]] :[[MoneyBee]] :[[Neocognitron]] :[[Nervous system network models]] :[[NETtalk (artificial neural network)]] :[[Neural backpropagation]] :[[Neural coding]] :[[Neural cryptography]] :[[Neural decoding]] :[[Neural gas]] :[[Neural Information Processing Systems]] :[[Neural modeling fields]] :[[Neural oscillation]] :[[Neurally controlled animat]] :[[Neuroevolution of augmenting topologies]] :[[Neuroplasticity]] :[[Ni1000]] :[[Nonspiking neurons]] :[[Nonsynaptic plasticity]] :[[Oja's rule]] :[[Optical neural network]] :[[Phase-of-firing code]] :[[Promoter based genetic algorithm]] :[[Pulse-coupled networks]] :[[Quantum neural network]] :[[Radial basis function]] :[[Radial basis function network]] :[[Random neural network]] :[[Recurrent neural network]] :[[Reentry (neural circuitry)]] :[[Reservoir computing]] :[[Rprop]] :[[Semantic neural network]] :[[Sigmoid function]] :[[SNARC]] :[[Softmax activation function]] :[[Spiking neural network]] :[[Stochastic neural network]] :[[Synaptic plasticity]] :[[Synaptic weight]] :[[Tensor product network]] :[[Time delay neural network]] :[[U-Matrix]] :[[Universal approximation theorem]] :[[Winner-take-all]] :[[Winnow (algorithm)]] ;Reinforcement learning :[[Reinforcement learning]] :[[Markov decision process]] :[[Bellman equation]] :[[Q-learning]] :[[Temporal difference learning]] :[[SARSA]] :[[Multi-armed bandit]] :[[Apprenticeship learning]] :[[Predictive learning]] ;Text Mining :[[Text mining]] :[[Natural language processing]] :[[Document classification]] :[[Bag of words model]] :[[N-gram]] :[[Part-of-speech tagging]] :[[Sentiment analysis]] :[[Information extraction]] :[[Topic model]] :[[Concept mining]] :[[Semantic analysis (machine learning)]] :[[Automatic summarization]] :[[Automatic distillation of structure]] :[[String kernel]] :[[Biomedical text mining]] :[[Never-Ending Language Learning]] ;Structure Mining :[[Structure mining]] :[[Structured learning]] :[[Structured prediction]] :[[Sequence mining]] :[[Sequence labeling]] :[[Process mining]] ;Advanced Learning Tasks :[[Multi-label classification]] :[[Classifier chains]] :[[Web mining]] :[[Anomaly detection]] :[[Anomaly Detection at Multiple Scales]] :[[Local outlier factor]] :[[Novelty detection]] :[[GSP Algorithm]] :[[Optimal matching]] :[[Record linkage]] :[[Meta learning (computer science)]] :[[Learning automata]] :[[Learning to rank]] :[[Multiple-instance learning]] :[[Statistical relational learning]] :[[Relational classification]] :[[Data stream mining]] :[[Alpha algorithm]] :[[Syntactic pattern recognition]] :[[Multispectral pattern recognition]] :[[Algorithmic learning theory]] :[[Deep learning]] :[[Bongard problem]] :[[Learning with errors]] :[[Parity learning]] :[[Inductive transfer]] :[[Granular computing]] :[[Conceptual clustering]] :[[Formal concept analysis]] :[[Biclustering]] :[[Information visualization]] :[[Co-occurrence networks]] ;Applications :[[Problem domain]] :[[Recommender system]] :[[Collaborative filtering]] :[[Profiling (information science)]] :[[Speech recognition]] :[[Stock forecast]] :[[Activity recognition]] :[[Data Analysis Techniques for Fraud Detection]] :[[Molecule mining]] :[[Predictive behavioral targeting]] :[[Proactive Discovery of Insider Threats Using Graph Analysis and Learning]] :[[Robot learning]] :[[Computer vision]] :[[Facial recognition system]] :[[Outlier detection]] :[[Anomaly detection]] :[[Novelty detection]] ;Software :[[R (programming language)]] :[[MapReduce]] :[[Oracle Data Mining]] :[[Pentaho]] :[[Mallet (software project)]] :[[Orange (software)]] :[[Learning Based Java]] :[[Scikit-learn]] :[[Waffles (machine learning)]] :[[Apache Mahout]] :[[Data Applied]] :[[Data Mining Extensions]] :[[ELKI]] :[[Feature Selection Toolbox]] :[[Monte Carlo Machine Learning Library (MCMLL)]] :[[Neural network software]] :[[Software mining]]</div></summary> <author><name>Dodgyb</name></author> </entry> <entry> <id>https://en.wikipedia.org/w/index.php?title=Trillium_Model&diff=493353636</id> <title>Trillium Model</title> <link rel="alternate" type="text/html" href="https://en.wikipedia.org/w/index.php?title=Trillium_Model&diff=493353636"/> <updated>2012-05-19T15:16:18Z</updated> <summary type="html">Dodgyb: /* External links */ broken link for Trillium Model, page no longer exists but the link provided is a mirror <hr /> <div>{{primarysources|date=January 2011}} The '''Trillium Model''', created by [[Bell Canada]], combines requirements from the [[ISO 9000]] series, the [[Capability Maturity Model|CMM]] for Software, and the [[Malcolm Baldrige National Quality Award|Malcolm Baldrige criteria]], with software quality standards from the [[IEEE]]. Trillium has a [[Telecommunication|telecommunications]] orientation and provides customer focus. The practices in the Trillium Model are derived from a [[benchmarking]] exercise which focused on all practices that would contribute to an organization's product development and support capability. The '''Trillium Model''' covers all aspects of the [[Software development process|software development life-cycle]], most system and product development and support activities, and a significant number of related marketing activities. Many of the practices described in the model can be applied directly to hardware development. == Objectives == The Trillium Model has been developed from a '''customer perspective''', as perceived in a competitive, commercial environment. The Model is used in a variety of ways: * to [[Benchmarking|'''benchmark''']] an organization's product development and support process capability against best practices in the industry, * in '''self-assessment mode''', to help identify opportunities for improvement within a product development organization, and * In '''pre-contractual negotiations''', to assist in selecting a supplier. This Model and its accompanying tools are not in themselves a [[New product development|product development process]] or [[Software development process|life-cycle model]]. Rather, the '''Trillium Model''' provides key [[Best practice|industry practices]] which can be used to improve an existing process or life-cycle == The Trillium Scale == The Trillium scale spans levels 1 through 5. The levels can be characterized in the following way: # '''Unstructured''': The [[Software development process|development process]] is [[Ad hoc|adhoc]]. Projects frequently cannot meet quality or schedule targets. Success, while possible, is based on individuals rather than on organizational infrastructure. '''(Risk - High) ''' # '''Repeatable and Project Oriented''': Individual project success is achieved through strong project management planning and control, with emphasis on [[requirements management]], [[estimation]] techniques, and [[configuration management]]. '''(Risk - Medium)''' # '''Defined and Process Oriented''': Processes are defined and utilized at the organizational level, although project customization is still permitted. Processes are controlled and improved. [[ISO 9000|ISO 9001]] requirements such as training and internal process [[Audit|auditing]] are incorporated. '''(Risk - Low)''' # '''Managed and Integrated''': Process instrumentation and analysis is used as a key mechanism for [[process improvement]]. [[Change management process|Process change management]] and [[Software review|defect prevention programs]] are integrated into processes. CASE tools are integrated into processes. '''(Risk - Lower) ''' # '''Fully Integrated''': Formal [[Methodology|methodologies]] are extensively used. Organizational [[wikt:Repository|repositories]] for development history and process are utilized and effective. '''(Risk - Lowest)''' == Architecture of the Trillium Model== The Trillium Model consists of Capability Areas, [[Technology roadmap|Roadmaps]] and [[Best practice|Practices]]. There are four different ways in which the Trillium Model is typically applied. The '''Capability Evaluation''' and '''Capability Joint-Assessment''' are two methods of evaluating an organization's product development and support process capability. A Capability Evaluation is the evaluation of a supplier by a second party, typically the customer. A Capability Joint Evaluation assumes an effective partnership relationship exists between the customer and supplier. == Benefits == For '''Customer organizations''', a higher capability means that: * the development organization is more responsive to customer and market demands, * the [[Product life cycle management|life-cycle cost of the product(s) is minimized]], and * end-user satisfaction is maximized. For the '''Development organization''', achieving a higher capability can result in: * lower development and maintenance costs, * shorter cycle time and development intervals, * an increased ability to achieve content and schedule commitments due to effective [[Identifying and Managing Project Risk|project risk]] analysis and effort [[Estimation in software engineering|estimation]], and * an increasing ability to meet quantifiable design and quality objectives at all stages of the [[Software development process|development process]] == CMM vs. Trillium == The '''Trillium Model''' covers all aspects of the [[Software development process|software development life-cycle]], most system and product development and support activities, and a significant number of related marketing activities. Although Trillium has been designed to be applied to embedded software systems such as telecommunications systems, much of the model can be applied to other segments of the software industry such as [[Management information system|'''Management Information Systems (MIS)''']]. The various differences between the Trillium Model and the [[Capability Maturity Model|CMM]] as given as follow: # Trillium mMdel architecture is based on roadmaps, rather than '''key process areas (KPAs)''' present in CMM # Trillium Model has a wider product perspective rather than only based on software process improvement # Trillium claims a wider coverage of capability impacting issues. # Trillium Model has orientation towards customer focus, technological maturity and telecommunication industry. == External links == * [http://www.sqi.gu.edu.au/trillium/trillium.html Trillium Model] * [http://www.bell.ca Bell Canada] [[Category:Bell Canada]] [[ar:賳賲賵匕噩丕賱鬲乇賷賱賷賵賲]]</div></summary> <author><name>Dodgyb</name></author> </entry> <entry> <id>https://en.wikipedia.org/w/index.php?title=User:Dodgyb&diff=492949656</id> <title>User:Dodgyb</title> <link rel="alternate" type="text/html" href="https://en.wikipedia.org/w/index.php?title=User:Dodgyb&diff=492949656"/> <updated>2012-05-17T00:48:29Z</updated> <summary type="html">Dodgyb: New user page through Outreach:ACIP <hr /> <div>A Geek and a Luddite</div></summary> <author><name>Dodgyb</name></author> </entry> </feed>

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Wikipedia - User contributions [en]