- From: Brand Niemann <bniemann@cox.net>
- Date: Fri, 13 Sep 2013 00:34:44 -0400
- To: "'Adam Sobieski'" <adamsobieski@hotmail.com>, <public-egovernance@w3.org>
- Message-ID: <014501ceb03a$91f2c000$b5d84000$@cox.net>
Adam and others, You may be interested in the work with Semantic Medline on the new Cray/Yarc Graph Computer at our recent conference: http://semanticommunity.info/Data_Science/Cloud_SOA_Semantics_and_Data_Science_Conference and my recent Post for Data Science DC: http://semanticommunity.info/Data_Science/Data_Science_DC#Story_2 Dr. Brand Niemann Director and Senior Data Scientist Semantic Community http://semanticommunity.info http://breakinggov.com/author/brand-niemann/ http://datacommunitydc.org/blog/2013/08/cloud-soa-semantics-and-data-science-conference/ 703-268-9314 From: Adam Sobieski [mailto:adamsobieski@hotmail.com] Sent: Thursday, September 12, 2013 12:41 AM To: public-egovernance@w3.org Subject: The DATA Act, Department of the Treasury, and Machine Learning Technologies e-Governance Community Group, At the nation‘s first federal open data policy conference, hosted by the Data Transparency Coalition <http://www.datacoalition.com/> , over 450 registrants had a first-ever opportunity to glimpse the future of open data policy at Data Transparency 2013 <http://www.datatransparency2013.com/> on September 10, 2013, in Washington - and see crucial reforms happening in real time. On May 21, 2013, the Digital Accountability and Transparency Act <http://datacoalition.com/issues/data-act.html> , or DATA Act, was simultaneously introduced in the U.S. House ( <http://beta.congress.gov/bill/113th-congress/house-bill/2061/> H.R. 2061) and Senate ( <http://beta.congress.gov/bill/113th-congress/senate-bill/994/> S. 994). The DATA Act modernizes the United States government, the computerization of the United States government, its processes, documents and data storage, and empowers the Department of the Treasury <http://www.treasury.gov> to set government-wide structured data formats for reports on awards, budget actions, payments, and financials. Analysis of government documents and processes, towards formulating new efficient and optimal systems and data architectures, of use throughout the federal government, can be enhanced by machine learning heuristics and technologies. Throughout the federal government are models, systems, taxonomies and ontologies pertinent to data transparency and pertinent to transactions and expenditures. The efficient and optimal cataloguing and indexing of public data, efficient and optimal taxonomies and ontology, can facilitate numerous software features and uses of public data, for instance the indexing, search and retrieval of public data as well as data routing systems such as pubsub systems. Machine learning technologies can enhance the analyses of government processes and data for formulating solutions including as aforementioned at the Department of the Treasury. Kind regards, Adam Sobieski P.S.: Some machine learning hyperlinks: <http://en.wikipedia.org/wiki/Machine_learning> Machine Learning Systems * <http://en.wikipedia.org/wiki/Supervised_learning> Supervised Learning * <http://en.wikipedia.org/wiki/AODE> AODE * <http://en.wikipedia.org/wiki/Artificial_neural_network> Artificial Neural Network * <http://en.wikipedia.org/wiki/Backpropagation> Backpropagation * <http://en.wikipedia.org/wiki/Bayesian_statistics> Bayesian Statistics * <http://en.wikipedia.org/wiki/Naive_Bayes_classifier> Naive Bayes Classifier * <http://en.wikipedia.org/wiki/Bayesian_network> Bayesian Network * <http://en.wikipedia.org/wiki/Bayesian_knowledge_base> Bayesian Knowledge Base * <http://en.wikipedia.org/wiki/Case-based_reasoning> Case-based Reasoning * <http://en.wikipedia.org/wiki/Decision_tree_learning> Decision Trees * <http://en.wikipedia.org/wiki/Inductive_logic_programming> Inductive Logic Programming * <http://en.wikipedia.org/wiki/Automatic_distillation_of_structure> Automatic Distillation of Structure * <http://en.wikipedia.org/wiki/Grammar_induction> Grammar Induction * Ontology Induction ( <http://dl-learner.org/Projects/DLLearner> DL-Learner) * <http://en.wikipedia.org/wiki/Syntactic_pattern_recognition> Syntactic Pattern Recognition * <http://vldb.org/conf/2007/papers/research/p998-bex.pdf> XML Schema Inference * <http://www.cogsys.wiai.uni-bamberg.de/schmid/uoshp/thesesuos/waltermann/~jwalterm/diplom/present/icml03.pdf> XSLT Induction * <http://en.wikipedia.org/wiki/Gaussian_process_regression> Gaussian Process Regression * <http://en.wikipedia.org/wiki/Gene_expression_programming> Gene Expression Programming * <http://en.wikipedia.org/wiki/Group_method_of_data_handling> Group Method of Data Handling * <http://en.wikipedia.org/wiki/Learning_Automata> Learning Automata * <http://en.wikipedia.org/wiki/Learning_Vector_Quantization> Learning Vector Quantization * <http://en.wikipedia.org/wiki/Logistic_Model_Tree> Logistic Model Tree * <http://en.wikipedia.org/wiki/Minimum_message_length> Minimum Message Length * <http://en.wikipedia.org/wiki/Lazy_learning> Lazy Learning * <http://en.wikipedia.org/wiki/Instance-based_learning> Instance-based Learning * <http://en.wikipedia.org/wiki/Nearest_neighbor_%28pattern_recognition%29> Nearest Neighbor Algorithm * <http://en.wikipedia.org/wiki/Analogical_modeling> Analogical Modeling * <http://en.wikipedia.org/wiki/Probably_approximately_correct_learning> Probably Approximately Correct Learning * <http://en.wikipedia.org/wiki/Ripple_down_rules> Ripple Down Rules * <http://en.wikipedia.org/wiki/Symbolic_machine_learning> Symbolic Machine Learning * <http://en.wikipedia.org/wiki/Subsymbolic_machine_learning> Subsymbolic Machine Learning * <http://en.wikipedia.org/wiki/Support_vector_machine> Support Vector Machine * <http://en.wikipedia.org/wiki/Random_forest> Random Forests * <http://en.wikipedia.org/wiki/Ensembles_of_classifiers> Ensembles of Classifiers * <http://en.wikipedia.org/wiki/Bootstrap_aggregating> Bootstrap Aggregating * <http://en.wikipedia.org/wiki/Boosting_%28meta-algorithm%29> Boosting * <http://en.wikipedia.org/wiki/Ordinal_classification> Ordinal Classification * <http://en.wikipedia.org/wiki/Regression_analysis> Regression Analysis * <http://en.wikipedia.org/wiki/Information_Fuzzy_Networks> Information Fuzzy Networks * <http://en.wikipedia.org/wiki/Statistical_classification> Statistical Classification * <http://en.wikipedia.org/wiki/ANOVA> ANOVA * <http://en.wikipedia.org/wiki/Linear_classifier> Linear Classifiers * <http://en.wikipedia.org/wiki/Fisher%27s_linear_discriminant> Fisher's Linear Discriminant * <http://en.wikipedia.org/wiki/Logistic_regression> Logistic Regression * <http://en.wikipedia.org/wiki/Naive_Bayes_classifier> Naive Bayes Classifier * <http://en.wikipedia.org/wiki/Perceptron> Perceptron * <http://en.wikipedia.org/wiki/Support_vector_machine> Support Vector Machines * <http://en.wikipedia.org/wiki/Quadratic_classifier> Quadratic Classifiers * <http://en.wikipedia.org/wiki/Nearest_neighbor_%28pattern_recognition%29> K-nearest Neighbor * <http://en.wikipedia.org/wiki/Boosting_%28machine_learning%29> Boosting * <http://en.wikipedia.org/wiki/Decision_tree_learning> Decision Trees * <http://en.wikipedia.org/wiki/C4.5> C4.5 * <http://en.wikipedia.org/wiki/Random_forest> Random Forests * <http://en.wikipedia.org/wiki/Bayesian_network> Bayesian Networks * <http://en.wikipedia.org/wiki/Hidden_Markov_model> Hidden Markov Models * <http://en.wikipedia.org/wiki/Unsupervised_learning> Unsupervised Learning * <http://en.wikipedia.org/wiki/Artificial_neural_network> Artificial Neural Network * <http://en.wikipedia.org/wiki/Data_clustering> Data Clustering * <http://en.wikipedia.org/wiki/Expectation-maximization_algorithm> Expectation-maximization Algorithm * <http://en.wikipedia.org/wiki/Self-organizing_map> Self-organizing Map * <http://en.wikipedia.org/wiki/Radial_basis_function_network> Radial Basis Function Network * <http://en.wikipedia.org/wiki/Vector_Quantization> Vector Quantization * <http://en.wikipedia.org/wiki/Generative_topographic_map> Generative Topographic Map * <http://en.wikipedia.org/wiki/Information_bottleneck_method> Information Bottleneck Method * <http://en.wikipedia.org/wiki/IBSEAD> IBSEAD * <http://en.wikipedia.org/wiki/Association_rule_learning> Association Rule Learning * <http://en.wikipedia.org/wiki/Apriori_algorithm> Apriori Algorithm * <http://en.wikipedia.org/wiki/Eclat_algorithm> Eclat Algorithm * <http://en.wikipedia.org/wiki/Association_rule_learning#FP-growth_algorithm> FP-growth Algorithm * <http://en.wikipedia.org/wiki/Hierarchical_clustering> Hierarchical Clustering * <http://en.wikipedia.org/wiki/Single-linkage_clustering> Single-linkage Clustering * <http://en.wikipedia.org/wiki/Conceptual_clustering> Conceptual Clustering * <http://en.wikipedia.org/wiki/Partition_of_a_set> Partitional Clustering * <http://en.wikipedia.org/wiki/K-means_algorithm> K-means Algorithm * <http://en.wikipedia.org/wiki/Fuzzy_clustering> Fuzzy Clustering * <http://en.wikipedia.org/wiki/Reinforcement_learning> Reinforcement Learning * <http://en.wikipedia.org/wiki/Temporal_difference_learning> Temporal Difference Learning * <http://en.wikipedia.org/wiki/Q-learning> Q-learning * <http://en.wikipedia.org/wiki/Learning_Automata> Learning Automata * <http://en.wikipedia.org/wiki/Monte_Carlo_Method> Monte Carlo Method * <http://en.wikipedia.org/wiki/SARSA> SARSA * Others * <http://en.wikipedia.org/wiki/Data_Pre-processing> Data Pre-processing
Received on Friday, 13 September 2013 04:35:17 UTC