Efficient Pruning Technique Based on Linear Relaxations

2005 ◽  
pp. 1-14 ◽  
Author(s):  
Yahia Lebbah ◽  
Claude Michel ◽  
Michel Rueher
Keyword(s):  
Linear Relaxations ◽  
Pruning Technique

Thermodynamic modeling of creep mechanical behaviour by non linear relaxations

1999 ◽  
Vol 6 (3) ◽  
pp. 229-235 ◽  
Author(s):  
Z. Ayadi ◽  
P. Marceron ◽  
J.-F. Schmitt ◽  
C. Cunat
Keyword(s):  
Thermodynamic Modeling ◽  
Mechanical Behaviour ◽  
Non Linear ◽  
Linear Relaxations

Computational Comparison of Piecewise−Linear Relaxations for Pooling Problems

2009 ◽  
Vol 48 (12) ◽  
pp. 5742-5766 ◽  
Author(s):  
Chrysanthos E. Gounaris ◽  
Ruth Misener ◽  
Christodoulos A. Floudas
Keyword(s):  
Piecewise Linear ◽  
Linear Relaxations ◽  
Computational Comparison

scholarly journals Visual e-commerce values filtering framework with spatial database metric

2020 ◽  
Vol 17 (3) ◽  
pp. 983-1006
Author(s):  
M. Kopecky ◽  
P. Vojtas
Keyword(s):  
User Behavior ◽  
Spatial Database ◽  
Synthetic Data ◽  
Combinatorial Search ◽  
Fuzzy Information ◽  
Additional Method ◽  
Analytic Hierarchy ◽  
Low Dimensions ◽  
Customer Preference ◽  
Linear Relaxations

Our customer preference model is based on aggregation of partly linear relaxations of value filters often used in e-commerce applications. Relaxation is motivated by the Analytic Hierarchy Processing method and combining fuzzy information in web accessible databases. In low dimensions our method is well suited also for data visualization. The process of translating models (user behavior) to programs (learned recommendation) is formalized by Challenge-Response Framework ChRF. ChRF resembles remote process call and reduction in combinatorial search problems. In our case, the model is automatically translated to a program using spatial database features. This enables us to define new metrics with visual motivation. We extend the conference paper with inductive ChRF, new representation of user and an additional method and metric. We provide experiments with synthetic data (items) and users.

A COMPARISON OF NEURAL NETWORK MODEL SELECTION STRATEGIES FOR THE PRICING OF S&P 500 STOCK INDEX OPTIONS

2007 ◽  
Vol 16 (06) ◽  
pp. 1093-1113 ◽  
Author(s):  
N. S. THOMAIDIS ◽  
V. S. TZASTOUDIS ◽  
G. D. DOUNIAS
Keyword(s):  
Neural Network ◽  
Model Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Information Criteria ◽  
Stock Index ◽  
Statistical Hypothesis ◽  
Neural Network Models ◽  
Index Options ◽  
Pruning Technique

This paper compares a number of neural network model selection approaches on the basis of pricing S&P 500 stock index options. For the choice of the optimal architecture of the neural network, we experiment with a “top-down” pruning technique as well as two “bottom-up” strategies that start with simple models and gradually complicate the architecture if data indicate so. We adopt methods that base model selection on statistical hypothesis testing and information criteria and we compare their performance to a simple heuristic pruning technique. In the first set of experiments, neural network models are employed to fit the entire options surface and in the second they are used as parts of a hybrid intelligence scheme that combines a neural network model with theoretical option-pricing hints.

Genetic-based Pruning Technique for Ant-Miner Classification Algorithm

2021 ◽  
Vol 11 (1) ◽  
pp. 304
Author(s):  
Hayder Naser Khraibet Al-Behadili ◽  
Ku Ruhana Ku-Mahamud ◽  
Rafid Sagban
Keyword(s):  
Classification Algorithm ◽  
Pruning Technique
Sign in / Sign up

Export Citation Format

Share Document