Search "+Peter Norvig +Automated machine learning +Class imbalance problem +Travelling salesman problem -Probability, Paradox, and the Reasonable Person Principle +Cross validation +Cluster analysis +Computer science -Ulrike von Luxburg +Class imbalance +scikit-learn +tf-idf +PyTorch +Boosting (machine learning) -Natural language processing"

Positive matches

• 0.235 + - Machine learning
• 0.044 + - skorch
• 0.038 + - Decision tree
• 0.038 + - tfidf
• 0.033 + - PyTorch-BigGraph
• 0.025 + - Gradient descent
• 0.024 + - Lexical feature selection
• 0.024 + - Recommender system
• 0.022 + - Data clustering
• 0.021 + - Text analysis
• 0.019 + - Random forest
• 0.017 + - Ron Meir
• 0.017 + - Yoav Freund
• 0.017 + - Rob Schapire
• 0.014 + - Python/Modules
• 0.013 + - Gensim
• 0.013 + - IPython notebook
• 0.012 + - Pointwise mutual information
• 0.012 + - NLTK
• 0.011 + - Poker

Negative matches

• 0.018 + - CNN
• 0.006 + - Residual leanrning
• 0.004 + - Residual Learning
• 0.003 + - Paper/Gatys2015
• 0.002 + - fastText
• 0.002 + - spaCy
• 0.002 + - String metric
• 0.002 + - NLP
• 0.002 + - Topic modeling
• 0.002 + - KenLM
• 0.002 + - Kyunghyun Cho
• 0.002 + - BERT
• 0.002 + - Long short-term memory
• 0.002 + - Latent Dirichlet allocation
• 0.002 + - Word embedding
• 0.002 + - scispaCy
• 0.002 + - Christopher D. Manning
• 0.002 + - Convolutional neural network
• 0.002 + - KoNLPy
• 0.002 + - Sentiment analysis