Search "+Peter Norvig +Automated machine learning +Class imbalance problem +Travelling salesman problem -Probability, Paradox, and the Reasonable Person Principle +Cross validation +Cluster analysis +Machine learning +Machine learning for healthcare +tf-idf +Boosting (machine learning) +Gensim +tfidf +Rob Schapire -Natural language processing"

Positive matches

• 0.126 + - Class imbalance
• 0.036 + - word2vec
• 0.030 + - Gradient descent
• 0.029 + - Text analysis
• 0.029 + - Recommender system
• 0.027 + - scikit-learn
• 0.024 + - Ulrike von Luxburg
• 0.024 + - Data clustering
• 0.019 + - Lexical feature selection
• 0.018 + - Ron Meir
• 0.018 + - Yoav Freund
• 0.017 + - Pointwise mutual information
• 0.015 + - Privacy and Deanonymization
• 0.014 + - Learning
• 0.013 + - NLTK
• 0.012 + - Future of work
• 0.011 + - Poker
• 0.011 + - Jupyter/notebook
• 0.011 + - IPython notebook
• 0.010 + - Regular expression

Negative matches

• 0.018 + - CNN
• 0.009 + - Residual Learning
• 0.004 + - Residual leanrning
• 0.002 + - fastText
• 0.002 + - BERT
• 0.002 + - spaCy
• 0.002 + - Latent Dirichlet allocation
• 0.002 + - String metric
• 0.002 + - NLP
• 0.002 + - Topic modeling
• 0.002 + - Kyunghyun Cho
• 0.002 + - Word segmentation
• 0.002 + - Long short-term memory
• 0.002 + - KenLM
• 0.002 + - Dialog act
• 0.002 + - BookNLP
• 0.002 + - Word embedding
• 0.002 + - scispaCy
• 0.002 + - Christopher D. Manning
• 0.002 + - Convolutional neural network