International Journal of Machine Learning (IJML) focuses on papers that cover and come under broad topics and is not limited to a particular aspect:

Classification, regression, recognition, and prediction
Problem-solving and planning
Reasoning and inference
Data mining
Web mining
Scientific discovery
Information retrieval
Natural language processing
Design and diagnosis
Vision and speech perception
Robotics and control
Combinatorial optimization
Game playing
Industrial, financial, and scientific applications of all kinds.
Supervised and unsupervised learning methods (including learning decision and regression trees, rules, connectionist networks, probabilistic networks and other statistical models, inductive logic programming, case-based methods, ensemble methods, clustering, etc.)
Reinforcement learning
Evolution-based methods
Explanation-based learning
Analogical learning methods
Automated knowledge acquisition
Learning from instruction
Visualization of patterns in data
Learning in integrated architectures
Multistrategy learning
Multi-agent learning and more