Unsupervised domain adaptation (UDA) is a weakly supervised learning technique that classifies images in the target domain when the source domain has labeled samples, and the target domain has unlabeled samples. Due to the complexity of imaging ...

Software defect prediction is a crucial discipline within the software development life cycle. Accurate identification of defective modules in software can result in time and cost savings for developers. The ELM algorithm offers the benefits of ...

News-driven quantitative trading (NQT) has been popularly studied in recent years. Most existing NQT methods are performed in a two-step paradigm, i.e., first analyzing markets by a financial prediction task and then making trading decisions, which is ...

Positive Unlabeled (PU) learning, which has a wide range of applications, is becoming increasingly prevalent. However, it suffers from problems such as data imbalance, selection bias, and prior agnostic in real scenarios. Existing studies focus on ...

Markowitz’s portfolio theory is the cornerstone of the risk-averse portfolio selection (RPS) problem, the core of which lies in minimizing the risk, i.e., a value calculated based on a portfolio risk matrix. Because the real risk matrix is unobservable,...

In view of the current demand for risk identification and classification prevention of bank outlets caused by the difficulty in identifying operational efficiency and wind control capability, a risk data measurement and warning classification model based ...

Click-through rate (CTR) prediction is the core problem of building advertising systems. Most existing state-of-the-art approaches model CTR prediction as binary classification problems, where displayed events with and without click feedbacks are ...

To date, factorization machines (FMs) have emerged as a powerful model in many applications. In this work, we study the training of FM with the logistic loss for binary classification, which is a nonlinear extension of the linear model with the logistic ...

Matrix factorization (MF) plays a key role in many applications such as recommender systems and computer vision, but MF may take long running time for handling large matrices commonly seen in the big data era. Many parallel techniques have been proposed ...