Copertina

Lasso Ridge based XGBoost and Deep_LSTM Help Tennis Players Perform better

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhai, Wankang, Wang, Yuhan
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning I.2
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

ResSurv: Cancer Survival Analysis Prediction Model Based on Residual Networks
di: Zhai, Wankang
Pubblicazione: (2024)

Enhancing Predictive Accuracy in Tennis: Integrating Fuzzy Logic and CV-GRNN for Dynamic Match Outcome and Player Momentum Analysis
di: Li, Kechen, et al.
Pubblicazione: (2025)

Learning Controllable and Diverse Player Behaviors in Multi-Agent Environments
di: Cilan, Atahan, et al.
Pubblicazione: (2025)

Learning To Help: Training Models to Assist Legacy Devices
di: Wu, Yu, et al.
Pubblicazione: (2024)

Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation
di: Kienzle, Daniel, et al.
Pubblicazione: (2025)

xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
di: Kraus, Maurice, et al.
Pubblicazione: (2024)

Large-Scale In-Game Outcome Forecasting for Match, Team and Players in Football using an Axial Transformer Neural Network
di: Horton, Michael, et al.
Pubblicazione: (2025)

Remaining Useful Life Estimation for Turbofan Engines: A Comparative Study of Classical, CNN, and LSTM Approaches
di: Goel, Astitva, et al.
Pubblicazione: (2026)

Your Data, My Model: Learning Who Really Helps in Federated Learning
di: Abdurakhmanova, Shamsiiat, et al.
Pubblicazione: (2024)

DeepDTF: Dual-Branch Transformer Fusion for Multi-Omics Anticancer Drug Response Prediction
di: Zhao, Yuhan, et al.
Pubblicazione: (2026)

Deep Generative Symbolic Regression
di: Holt, Samuel, et al.
Pubblicazione: (2023)

A Hierarchical conv-LSTM and LLM Integrated Model for Holistic Stock Forecasting
di: Chakraborty, Arya, et al.
Pubblicazione: (2024)

Spatio-Temporal Forecasting of Retaining Wall Deformation: Mitigating Error Accumulation via Multi-Resolution ConvLSTM Stacking Ensemble
di: Kim, Jihoon, et al.
Pubblicazione: (2026)

Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models
di: Nicolau, Marcio, et al.
Pubblicazione: (2020)

CNN-LSTM Hybrid Architecture for Over-the-Air Automatic Modulation Classification Using SDR
di: Padhya, Dinanath, et al.
Pubblicazione: (2025)

Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation
di: Kuleshov, Volodymyr, et al.
Pubblicazione: (2021)

STAR : Bridging Statistical and Agentic Reasoning for Large Model Performance Prediction
di: Wang, Xiaoxiao, et al.
Pubblicazione: (2026)

Misconduct in Post-Selections and Deep Learning
di: Weng, Juyang
Pubblicazione: (2024)

Evaluating the effectiveness of predicting covariates in LSTM Networks for Time Series Forecasting
di: Davies, Gareth
Pubblicazione: (2024)

KAN vs LSTM Performance in Time Series Forecasting
di: Rather, Tabish Ali, et al.
Pubblicazione: (2025)

When Does Adaptive Guidance Help? Belief-Aware Privileged Distillation for Autonomous Driving Under Partial Observability
di: Haklidir, Mehmet
Pubblicazione: (2026)

Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition
di: Hassani, Zeinab, et al.
Pubblicazione: (2025)

Transformer Scalability Crisis: The First Comprehensive Empirical Analysis of Performance Walls in Modern Language Models
di: Moghadasi, Mahdi Naser, et al.
Pubblicazione: (2026)

Research on a hybrid LSTM-CNN-Attention model for text-based web content classification
di: Kuz, Mykola, et al.
Pubblicazione: (2025)

Autonomous Deep Agent
di: Yu, Amy, et al.
Pubblicazione: (2025)

Natural Language Can Help Bridge the Sim2Real Gap
di: Yu, Albert, et al.
Pubblicazione: (2024)

Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost
di: Moorthy, Padmavathi
Pubblicazione: (2025)

EchoLSTM: A Self-Reflective Recurrent Network for Stabilizing Long-Range Memory
di: K, Prasanth K, et al.
Pubblicazione: (2025)

An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization
di: Xie, Lunchen, et al.
Pubblicazione: (2021)

Compositional Concept-Based Neuron-Level Interpretability for Deep Reinforcement Learning
di: Jiang, Zeyu, et al.
Pubblicazione: (2025)

When Does Global Attention Help? A Unified Empirical Study on Atomistic Graph Learning
di: Chowdhury, Arindam, et al.
Pubblicazione: (2025)

Deep Variational Inference Symbolic Regression
di: Butterworth, James, et al.
Pubblicazione: (2026)

Static Seeding and Clustering of LSTM Embeddings to Learn from Loosely Time-Decoupled Events
di: Manasseh, Christian, et al.
Pubblicazione: (2022)

A Comparative Survey of PyTorch vs TensorFlow for Deep Learning: Usability, Performance, and Deployment Trade-offs
di: Alawi, Zakariya Ba
Pubblicazione: (2025)

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes
di: Jayawardhana, Mayuka, et al.
Pubblicazione: (2025)

Terrier: A Deep Learning Repeat Classifier
di: Turnbull, Robert, et al.
Pubblicazione: (2025)

Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectories
di: Ji, Yuhan, et al.
Pubblicazione: (2024)

DeepPersona: A Generative Engine for Scaling Deep Synthetic Personas
di: Wang, Zhen, et al.
Pubblicazione: (2025)

Time Aggregation Features for XGBoost Models
di: Pinchuk, Mykola
Pubblicazione: (2026)

Evaluation of QCNN-LSTM for Disability Forecasting in Multiple Sclerosis Using Sequential Multisequence MRI
di: Mayfield, John D., et al.
Pubblicazione: (2024)