joeyrichar

4 ms·

1. 1.▲
   by joeyrichar 13y ago

   This is exactly what we're enabling with our ML Platform (currently in private beta). Such a system needs to be built on top of fast & scalable ML technology with smart & efficient tuning/optimization. Would love to hear
2. 2.▲
   Bitcoin ≈ MySpace(5nf5.blogspot.com)

   13 points by joeyrichar 13y ago|11 comments
3. 3.▲
   by joeyrichar 13y ago

   Thanks for the comments. (1) We chose to do our original benchmarks against R, Weka and sklearn because these are the tools that the vast majority of people currently use. You'd be amazed how many companies use Weka! That said, we do benchm
4. 4.▲
   by joeyrichar 13y ago

   (1) The speed-up is achieved on a single core (multithreaded). The tiny memory footprint enables us to do embedded learning (e.g., on an ARM chip). We also have a distributed version of WiseRF in development (stay tuned!). (2) Soon, we'll
5. 5.▲
   by joeyrichar 13y ago

   As textminer says, RFs are really nice in that there are few parameters to tune, and the results typically are not that sensitive to the choice of those parameters (contrasted to, say, SVMs, where you can get killed in performance with a
6. 6.▲
   by joeyrichar 13y ago

   Ozten, we completely agree which is why we also provide an on premise version of our Machine Intelligence Engine. Our mission is to democratize ML and allow companies to easily deploy it in production. Thanks for the interest and feedback!