منابع مشابه
Simulation assisted machine learning
Predicting how a proposed cancer treatment will affect a given tumor can be cast as a machine learning problem, but the complexity of biological systems, the number of potentially relevant genomic and clinical features, and the lack of very large scale patient data repositories make this a unique challenge. “Pure data” approaches to this problem are underpowered to detect combinatorially comple...
متن کامل1988-Simulation-Assisted Inductive Learning
Learning by induction can require a large number of training examples. We show the power of using a simulator to generate training data and test data in learning rules for an expert system. The induction program is RL, a simplified version of Meta-DENDRAL. The expert system is ABLE, a rule-based system that identifies and locates errors in particle beam lines used in high energy physics. A simu...
متن کاملMachine Learning-Assisted Binary Code Analysis
Binary code analysis is a foundational technique in the areas of computer security, performance modeling, and program instrumentation. In computer security, such analysis can provide the basis for detecting, understanding and controlling malicious code. Any analysis of malicious program requires as a first step precisely locating the Function Entry Points (FEPs, the starting byte of each functi...
متن کاملUsing Machine Learning to Guide Architecture Simulation
An essential step in designing a new computer architecture is the careful examination of different design options. It is critical that computer architects have efficient means by which they may estimate the impact of various design options on the overall machine. This task is complicated by the fact that different programs, and even different parts of the same program, may have distinct behavio...
متن کاملSimulation of Scour Pattern Around Cross-Vane Structures Using Outlier Robust Extreme Learning Machine
In this research, the scour hole depth at the downstream of cross-vane structures with different shapes (i.e., J, I, U, and W) was simulated utilizing a modern artificial intelligence method entitled "Outlier Robust Extreme Learning Machine (ORELM)". The observational data were divided into two groups: training (70%) and test (30%). Then, using the input parameters including the ratio of the st...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2019
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btz199