Essays about: "Ridge Regression"
Showing result 1 - 5 of 53 essays containing the words Ridge Regression.
-
1. Initial Development and Validation of Language-Based Assessments for Meaningful Change
University essay from Lunds universitet/Institutionen för psykologiAbstract : Meaningful change has been discussed in multiple studies, with the recurring question of how it could be conceptualized and assessed to identify what determines meaningful change and where it occurs. Previous studies have conducted statistical analyses based on traditional rating scales (i.e., the PHQ-9) to assess meaningful change. READ MORE
-
2. Regularization Methods and High Dimensional Data: A Comparative Study Based on Frequentist and Bayesian Methods
University essay from Lunds universitet/Statistiska institutionenAbstract : As the amount of high dimensional data becomes increasingly accessible and common, the need for reliable methods to combat problems such as overfitting and multicollinearity increases. Models need to be able to manage large data sets where predictor variables often outnumber the amount of observations. READ MORE
-
3. Kernel Methods for Regression
University essay from Linnéuniversitetet/Institutionen för matematik (MA)Abstract : Kernel methods are a well-studied approach for addressing regression problems by implicitly mapping input variables into possibly infinite-dimensional feature spaces, particularly in cases where standard linear regression fails to capture non-linear relationships in data. Therefore, the choice between standard linear regression and kernel regression can be seen as a tradeoff between constraints on the number of features and the number of training samples. READ MORE
-
4. Regression with Bayesian Confidence Propagating Neural Networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Bayesian Confidence Propagating Neural Networks (BCPNNs) are biologically inspired artificial neural networks. These networks have been modeled to account for brain-like aspects such as modular architecture, divisive normalization, sparse connectivity, and Hebbian learning. READ MORE
-
5. Quantum Algorithms for Feature Selection and Compressed Feature Representation of Data
University essay from KTH/FysikAbstract : Quantum computing has emerged as a new field that may have the potential to revolutionize the landscape of information processing and computational power, although physically constructing quantum hardware has proven difficult,and quantum computers in the current Noisy Intermediate Scale Quantum (NISQ) era are error prone and limited in the number of qubits they contain.A sub-field within quantum algorithms research which holds potential for the NISQ era, and which has seen increasing activity in recent years, is quantum machine learning, where researchers apply approaches from classical machine learning to quantum computing algorithms and explore the interplay between the two. READ MORE
