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, over 450 registrants had a first-ever opportunity to glimpse the future of open data policy at Data Transparency 2013 on September 10, 2013, in Washington - and see crucial reforms happening in real time.

 

On May 21, 2013, the Digital Accountability and Transparency Act, or DATA Act, was simultaneously introduced in the U.S. House (H.R. 2061) and Senate (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 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:

 

Machine Learning Systems Supervised Learning AODE 
Artificial Neural Network Backpropagation 

Bayesian Statistics Naive Bayes Classifier 
Bayesian Network 
Bayesian Knowledge Base 

Case-based Reasoning 
Decision Trees 
Inductive Logic Programming Automatic Distillation of Structure 
Grammar Induction 
Ontology Induction (DL-Learner) 
Syntactic Pattern Recognition 
XML Schema Inference 
XSLT Induction 

Gaussian Process Regression 
Gene Expression Programming 
Group Method of Data Handling 
Learning Automata 
Learning Vector Quantization 
Logistic Model Tree 
Minimum Message Length 
Lazy Learning 
Instance-based Learning Nearest Neighbor Algorithm 
Analogical Modeling 

Probably Approximately Correct Learning 
Ripple Down Rules 
Symbolic Machine Learning 
Subsymbolic Machine Learning 
Support Vector Machine 
Random Forests 
Ensembles of Classifiers Bootstrap Aggregating 
Boosting 

Ordinal Classification 
Regression Analysis 
Information Fuzzy Networks 

Statistical Classification ANOVA 
Linear Classifiers Fisher's Linear Discriminant 
Logistic Regression 
Naive Bayes Classifier 
Perceptron 
Support Vector Machines 

Quadratic Classifiers 
K-nearest Neighbor 
Boosting 
Decision Trees C4.5 
Random Forests 

Bayesian Networks 
Hidden Markov Models 

Unsupervised Learning Artificial Neural Network 
Data Clustering 
Expectation-maximization Algorithm 
Self-organizing Map 
Radial Basis Function Network 
Vector Quantization 
Generative Topographic Map 
Information Bottleneck Method 
IBSEAD 

Association Rule Learning Apriori Algorithm 
Eclat Algorithm 
FP-growth Algorithm 

Hierarchical Clustering Single-linkage Clustering 
Conceptual Clustering 

Partitional Clustering K-means Algorithm 
Fuzzy Clustering 

Reinforcement Learning Temporal Difference Learning 
Q-learning 
Learning Automata 
Monte Carlo Method 
SARSA 

Others Data Pre-processing