Papers Multi-target regression
“Multi-target regression” 태그가 달린 논문 38편 · 필터 해제
Learning the Pareto Front with Hypernetworks
Multi-objective optimization (MOO) problems are prevalent in machine learning. These problems have a set of optimal solutions, called the Pareto front, where each point on the front represents a different trade-off betwe…
FairnessMultiobjective OptimizationMulti-target regressionMulti-Task Learning+1DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection
Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes. To secure government revenue without interrupting legitimate …
Fraud DetectionMulti-target regressionValue predictionFeature Ranking for Semi-supervised Learning
The data made available for analysis are becoming more and more complex along several directions: high dimensionality, number of examples and the amount of labels per example. This poses a variety of challenges for the e…
ClassificationGeneral ClassificationHierarchical Multi-label ClassificationMulti-Label Classification+3Approaches For Multi-View Redescription Mining
The task of redescription mining explores ways to re-describe different subsets of entities contained in a dataset and to reveal non-trivial associations between different subsets of attributes, called views. This intere…
AttributeBIG-bench Machine LearningClusteringMulti-Label Classification+2Deep Multimodal Transfer-Learned Regression in Data-Poor Domains
In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc. This task can be further complicated by a lack of sufficient d…
Multi-target regressionregressionBoosting on the shoulders of giants in quantum device calibration
Traditional machine learning applications, such as optical character recognition, arose from the inability to explicitly program a computer to perform a routine task. In this context, learning algorithms usually derive a…
BIG-bench Machine LearningFew-Shot LearningMulti-target regressionOptical Character RecognitionPowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning
With the increase in the complexity of chip designs, VLSI physical design has become a time-consuming task, which is an iterative design process. Power planning is that part of the floorplanning in VLSI physical design w…
Multi-target regressionMulti-target regression via output space quantization
Multi-target regression is concerned with the prediction of multiple continuous target variables using a shared set of predictors. Two key challenges in multi-target regression are: (a) modelling target dependencies and …
Computational EfficiencyMulti-target regressionQuantizationregressionOptimistic bounds for multi-output prediction
We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set. This includes a range of important problems in Machine Learning including multi-t…
General ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra
Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) i…
Multi-target regressionregressionOptimistic bounds for multi-output learning
We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set. This includes a range of important problems in Machine Learning including multi-t…
General ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1INTEL-TAU: A Color Constancy Dataset
In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination est…
Color ConstancyFew-Shot Camera-Adaptive Color ConstancyImage DeclippingMulti-target regressionTowards meta-learning for multi-target regression problems
Several multi-target regression methods were devel-oped in the last years aiming at improving predictive performanceby exploring inter-target correlation within the problem. However, none of these methods outperforms the…
Meta-LearningMulti-target regressionregressionOutlier Robust Extreme Learning Machine for Multi-Target Regression
The popularity of algorithms based on Extreme Learning Machine (ELM), which can be used to train Single Layer Feedforward Neural Networks (SLFN), has increased in the past years. They have been successfully applied to a …
Multi-target regressionregressionOnline Multi-target regression trees with stacked leaf models
One of the current challenges in machine learning is how to deal with data coming at increasing rates in data streams. New predictive learning strategies are needed to cope with the high throughput data and concept drift…
Multi-target regressionNeural Network CompressionregressionOrders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulation
Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity of the chemical mechanisms: systems of …
GPUMulti-target regressionMulti-Target Regression via Random Linear Target Combinations
Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It arises in several interesting industrial and environmental applic…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-target regression+1Multi-Target Regression via Input Space Expansion: Treating Targets as Inputs
In many practical applications of supervised learning the task involves the prediction of multiple target variables from a common set of input variables. When the prediction targets are binary the task is called multi-la…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-target regression+2