# Apache SystemML Framework

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A Apache SystemML Framework is a machine learning framework ...

**Example(s):**- SystemML v0.14.0 (2017-06-26) for Spark 2.

**Counter-Example(s):****See:**Declarative Programming, Apache Spark.

## References

### 2017

Descriptive Statistics Univariate Statistics Bivariate Statistics Stratified Bivariate Statistics Classification Multinomial Logistic Regression Support Vector Machines Binary-Class Support Vector Machines Multi-Class Support Vector Machines Naive Bayes Decision Trees Random Forests Clustering K-Means Clustering Regression Linear Regression Stepwise Linear Regression Generalized Linear Models Stepwise Generalized Linear Regression Regression Scoring and Prediction Matrix Factorization Principal Component Analysis Matrix Completion via Alternating Minimizations Survival Analysis Kaplan-Meier Survival Analysis Cox Proportional Hazard Regression Model

### 2016

- (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/Apache_SystemML Retrieved:2016-6-3.

### 2014

- http://researcher.watson.ibm.com/researcher/view_group.php?id=3174
- Declarative large-scale machine learning (ML) in SystemML aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations to distributed computations on MapReduce or Spark. ML algorithms are expressed in an R-like syntax, that includes linear algebra primitives, statistical functions, and ML-specific constructs. This high-level language significantly increases the productivity of data scientists as it provides (1) full flexibility in expressing custom analytics, and (2) data independence from the underlying input formats and physical data representations. Automatic optimization according to data and cluster characteristics ensures both efficiency and scalability. As such, SystemML differs from existing work on large-scale ML libraries, which mostly provide fixed algorithms and runtime plans.

### 2011

- (Ghoting et al., 2011) ⇒ Amol Ghoting, Rajasekar Krishnamurthy, Edwin Pednault, Berthold Reinwald, Vikas Sindhwani, Shirish Tatikonda, Yuanyuan Tian, and Shivakumar Vaithyanathan. (2011). “SystemML: Declarative Machine Learning on MapReduce.” In: Proceedings of the 2011 IEEE 27th International Conference on Data Engineering. ISBN:978-1-4244-8959-6 doi:10.1109/ICDE.2011.5767930