Results 111 to 120 of about 1,169,808 (377)

Decision Tree Instability and Active Learning [PDF]

, 2007
Decision tree learning algorithms produce accurate models that can be interpreted by domain experts. However, these algorithms are known to be unstable --- they can produce drastically different hypotheses from training sets that differ just slightly. This instability undermines the objective of extracting knowledge from the trees.
Robert C. Holte, Kenneth Dwyer
openaire   +2 more sources

Direct Electron Detection Electron Energy‐Loss Spectroscopy: Speeding Up 2D Analytical In Situ Transmission Electron Microscopy for Aluminum Alloys

Advanced Engineering Materials, EarlyView.
Scanning transmission electron microscopy imaging techniques are an essential tool to document dynamic developments, such as precipitation in aluminum alloys, during in situ heating experiments using transmission electron microscopy. However, in many cases, chemical information is required to interpret complex nanoscale processes.
Evelin Fisslthaler, Martina Dienstleder, Judith Lammer, Moritz Theissing, Daniel Knez   +4 more
wiley   +1 more source

Learning Optimal Dynamic Treatment Regime from Observational Clinical Data through Reinforcement Learning

Machine Learning and Knowledge Extraction
In medicine, dynamic treatment regimes (DTRs) have emerged to guide personalized treatment decisions for patients, accounting for their unique characteristics. However, existing methods for determining optimal DTRs face limitations, often due to reliance
Seyum Abebe, Irene Poli, Roger D. Jones, Debora Slanzi   +3 more
doaj   +1 more source

Machine Learning‐Based Design of 3D‐Printable Microneedles for Enhanced Tissue Anchoring with Reduced Tissue Damage

Advanced Engineering Materials, EarlyView.
A machine learning‐driven framework is introduced to optimize 3D‐printable microneedles for enhanced tissue anchoring and reduced insertion damage. The optimized design achieves a 6.0‐fold improvement in pull‐out‐to‐penetration energy ratio over conventional shapes.
Jegyeong Ryu, Jiyeop Kim, Chan Wook Park, Hae‐Yoon Kim, Deukhee Lee, Amy Kyungwon Han   +5 more
wiley   +1 more source

Deepfake Image Classification Using Decision (Binary) Tree Deep Learning

Journal of Sensor and Actuator Networks
The unprecedented rise of deepfake technologies, leveraging sophisticated AI like Generative Adversarial Networks (GANs) and diffusion-based models, presents both opportunities and challenges in terms of digital media authenticity.
Mariam Alrajeh, Aida Al-Samawi
doaj   +1 more source

High‐Entropy Magnetism of Murunskite

Advanced Functional Materials, EarlyView.
The study of murunskite (K2FeCu3S4) reveals that its magnetic and orbital order emerges in a simple I4/mmm crystal structure with complete disorder in the transition metal positions. Mixed‐valence Fe ions randomly occupy 1/4 of the tetrahedral sites, with the remaining 3/4 being filled by non‐magnetic Cu+ ions.
Davor Tolj, Priyanka Reddy, Ivica Živković, Luka Akšamović, Jian Rui Soh, Kamila Komȩdera, Izabela Biało, Naveen Kumar Chogondahalli Muniraju, Trpimir Ivšić, Mario Novak, Oksana Zaharko, Clemens Ritter, Thomas LaGrange, Wojciech Tabiś, Ivo Batistić, László Forró, Henrik M. Rønnow, Denis K. Sunko, Neven Barišić   +18 more
wiley   +1 more source

Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories

, 2018
With the explosion in the availability of spatio-temporal tracking data in modern sports, there is an enormous opportunity to better analyse, learn and predict important events in adversarial group environments.
Denman, Simon, Fernando, Tharindu, Fookes, Clinton, Sridharan, Sridha   +3 more
core   +1 more source

Flow‐Induced Vascular Remodeling on‐Chip: Implications for Anti‐VEGF Therapy

Advanced Functional Materials, EarlyView.
Flow‐induced vascular remodeling plays a critical role in network stabilization and function. Using a vasculature‐on‐chip system, this study reveals how physiological VEGF levels and flow affect vascular remodeling and provides insights into tumor vessel normalization.
Fatemeh Mirzapour‐Shafiyi, Elias Huber, Leonie Karr, Johnny Tong, Andreas Richard Bausch, Friedrich Christian Simmel, Karen Alim   +6 more
wiley   +1 more source

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service [PDF]

, 2017
In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to ...
Cools, Mario, Farooq, Bilal, Saadi, Ismaïl, Teller, Jacques, Wong, Melvin   +4 more
core  

Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving

, 2019
Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users.
Driggs-Campbell, Katherine, Hoel, Carl-Johan, Kochenderfer, Mykel J., Laine, Leo, Wolff, Krister   +4 more
core   +1 more source