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 +Ulrike von Luxburg +Gradient descent -Neural network"

Positive matches

• 0.126 + - Class imbalance
• 0.033 + - Text analysis
• 0.033 + - tfidf
• 0.029 + - Recommender system
• 0.027 + - word2vec
• 0.025 + - scikit-learn
• 0.025 + - Neuron
• 0.024 + - Data clustering
• 0.022 + - Lexical feature selection
• 0.019 + - Pointwise mutual information
• 0.018 + - Ron Meir
• 0.018 + - Yoav Freund
• 0.018 + - Rob Schapire
• 0.015 + - Privacy and Deanonymization
• 0.014 + - Learning
• 0.013 + - NLTK
• 0.012 + - Jupyter/notebook
• 0.011 + - Poker
• 0.011 + - IPython notebook
• 0.011 + - Regular expression

Negative matches

• 0.022 + - Paper/Srivastava2014
• 0.007 + - Jack Terwilliger
• 0.007 + - Neural Turing machine
• 0.006 + - Liquid state machine
• 0.005 + - Dropout
• 0.005 + - Recurrent neural network
• 0.004 + - Ilya Sutskever
• 0.002 + - Skip-gram
• 0.001 + - Long short-term memory
• 0.001 + - Wolfgang Maass
• 0.001 + - Graph embedding
• 0.001 + - Paper/Graves2014NTM
• 0.001 + - Greg Wayne
• 0.001 + - Paper/Cooijmans2016
• 0.001 + - Sentiment analysis
• 0.001 + - Continuous embedding
• 0.001 + - Ivo Danihelka
• 0.001 + - String metric
• 0.001 + - Google
• 0.000 + - Paper