对多任务回归特征空间的贝叶斯式学习
Carlos Sevilla-Salcedo1, Ascensión Gallardo-Antolín2, Vanessa Gómez-Verdejo2
1Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés, 28911, Madrid, Spain; Department of Computer Science, Aalto University, Espoo, 02150, Helsinki, Finland.
概括
这项研究提出了一个新的多任务回归模型,RFF-BLR,使用随机里埃特征和贝叶斯优化. 它通过有效地学习复杂的关系来实现更好的性能,特别是在有限的数据上.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算统计学 计算统计学
背景情况:
- 多任务学习 (MTL) 旨在通过同时学习多个相关任务来提高概括性.
- 有限制的架构复杂性对于高效和可扩展的机器学习模型至关重要.
- 内核方法提供了稳定性,但在计算上可能很昂贵.
研究的目的:
- 引入一种新的多任务回归模型,RFF-BLR,具有受约束的架构复杂性.
- 为了利用随机的福里埃特征 (RFF) 来在神经网络中近似内核方法.
- 采用贝叶斯式公式来优化模型重量和促进稀疏性.
主要方法:
- 拟议的RFF-BLR模型使用单个隐藏层,其中RFF单元接近一个辐射基函数 (RBF) 内核.
- 贝叶斯式方法优化了权重,使任务能够在训练期间相互作用.
- 通过选择隐藏单位的紧子集来促进多输出稀疏性,以实现常见的非线性映射.
主要成果:
- 在多任务非线性回归中,RFF-BLR在多任务非线性回归中比最先进的方法显著提高了性能.
- 该模型在具有小规模培训数据集的场景中尤为出色.
- 基于RFF的隐藏层提供了内核方法特有的稳定性.
结论:
- RFF-BLR框架为控制复杂性的多任务非线性回归提供了一种有效的方法.
- 结合RFF和贝叶斯优化,可以实现高效的学习和特征选择.
- 这种方法对在有限的数据上需要强大的性能的应用有希望.
相关概念视频
01:22Multi-input and Multi-variable systems
105
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
105
01:25Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
01:14Multicompartment Models: Overview
115
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
115
01:27Associative Learning
322
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Classical conditioning, also known...
322
01:11Residuals and Least-Squares Property
7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
64
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
64