A deep embedding forest-based (DEF) model for improving on-line serving time for classification learning methods and other tasks such as, for example, predicting user selection of search results provided in response to a query or for image, speech or text recognition. Initially, a deep neural network (DNN) model is trained to determine parameters of an embedding layer, a stacking layer, deep layers and a scoring layer thereby reducing high dimensional features. After training the DNN model, the parameters of the deep layers and the scoring layer of the DNN model and discarded and the parameters of the embedding layer and the stacking layer are extracted. The extracted parameters from the DNN model then initialize parameters of an embedding layer and a stacking layer of the DEF model such that only a forest layer of the DEF model is then required to be trained. Output from the DEF model is stored in computer memory.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: in response to received input, accessing, via a model manager, a Deep Neural Network (DNN) model comprising an embedding layer, stacking layer, deep layers and a scoring layer; initiating training of the DNN model via the model manager to determine parameters of the embedding layer, the stacking layer, the deep layers and the scoring layer; initializing parameters of an embedding layer of a Deep Embedding Forest (DEF) model with parameters extracted from the deep layers of the trained DNN model; initializing parameters of a stacking layer of the DEF model with parameters extracted from the stacking layer of the trained DNN model; and producing an output from the DEF model to computer memory. 2. The method of claim 1, further comprising, after training the DNN model, discarding the parameters of the deep layers and the scoring layer of the DNN model. 3. The method of claim 1 further comprising: applying the initialized parameters of the embedding layer of the DEF model to features of the DEF model to generate a stacking vector; providing the stacking vector to a forest layer of the DEF model; and training only the forest layer of the DEF model, thereby using less memory and computation power. 4. The method of claim 1 wherein the DNN model reduces high dimensional features in the range of hundreds of thousands to millions to lower dimensional vectors of less than 10 thousand for initializing a forest layer. 5. The method of claim 1 further comprising discarding the parameters of the deep layers and the scoring layer of the DNN model and replacing the deep layers and the scoring layer of the DNN model with a forest layer to thereby define the DEF model. 6. The method of claim 5 further comprising initializing the forest layer of the DEF model utilizing a gradient boosting machine. 7. The method of claim 6 wherein accessing the DNN model comprises accessing a deep crossing model, the deep layers of the DNN model being multiple residual units of the deep crossing model, training the DNN comprises training the deep crossing model, and initializing the forest layer of the DEF model utilizing a gradient boosting machine comprises utilizing XEBoost to initializing the forest layer of the DEF model. 8. The method of claim 6 further comprising jointly optimizing the parameters of both the embedding layer and the forest layer of the DEF model after fully initializing the DEF model as a result of initializing the embedding and stacking layers of the DEF model and initializing the forest layer of the DEF model utilizing the gradient boosting machine. 9. The method of claim 1 wherein accessing a DNN model comprises accessing a deep crossing model and the deep layers are multiple residual units. 10. The method of claim 1 further comprising jointly optimizing the parameters of both the embedding layer and the forest layer of the DEF model utilizing partial fuzzification. 11. The method of claim 1 further comprising replacing the deep layers and the scoring layer of the DNN model with a forest layer to thereby define the DEF model, initializing the forest layer of the DEF model utilizing a gradient boosting machine, and jointly optimizing the parameters of both the embedding layer and the forest layer of the DEF model after fully initializing the DEF model as a result of initializing the embedding and stacking layers of the DEF model and initializing the forest layer of the DEF model utilizing the gradient boosting machine. 12. A system for implementing a deep embedding forest model, comprising: at least one processor; an operating environment executing using the at least one processor; and a model manager configured to perform actions comprising: accessing a Deep Neural Network (DNN) model comprising an embedding layer, stacking layer, deep layers and a scoring layer; training the DNN model to determine parameters of the embedding layer, stacking layer, the deep layers and the scoring layer; initializing parameters of an embedding layer of a Deep Embedding Forest (DEF) model with parameters extracted from the deep layers of the trained DNN model; initializing parameters of a stacking layer of the DEF model with parameters extracted from the stacking layer of the trained DNN model; applying the initialized parameters of the embedding layer of the DEF model to features of the DEF model to generate a stacking vector; providing the stacking vector to a forest layer of the DEF model; and training only the forest layer of the DEF model. 13. The system of claim 12 further comprising discarding the parameters of the deep layers and the scoring layer of the DNN model and replacing the deep layers and the scoring layer of the DNN model with a forest layer to thereby define the DEF model. 14. The system of claim 12 further comprising initializing the forest layer of the DEF model utilizing a gradient boosting machine. 15. The system of claim 12 further comprising jointly optimizing the parameters of both the embedding layer and the forest layer of the DEF model after fully initializing the DEF model as a result of initializing the embedding and stacking layers of the DEF model and initializing the forest layer of the DEF model utilizing the gradient boosting machine. 16. A computer-readable storage medium storing computer-executable instructions that when executed using a processor perform actions, comprising: accessing, via a model manager, a Deep Neural Network (DNN) model comprising an embedding layer, stacking layer, deep layers and a scoring layer; training the DNN model to determine parameters of the embedding layer, stacking layer, the deep layers and the scoring layer, wherein training the DNN model includes tuning the DNN model using back propagation; initializing parameters of an embedding layer of a Deep Embedding Forest (DEF) model with parameters extracted from the deep layers of the trained DNN model; initializing parameters of a stacking layer of the DEF model with parameters extracted from the stacking layer of the trained DNN model; and initializing a forest layer of the DEF model utilizing a gradient boost machine; and producing an output from the DEF model to computer memory. 17. The computer-readable storage medium of claim 16 further comprising: applying the initialized parameters of the embedding layer of the DEF model to features of the DEF model to generate a stacking vector; providing the stacking vector to a forest layer of the DEF model; and training only the forest layer of the DEF model, thereby using less memory and computation power. 18. The computer-readable storage medium of claim 16 further comprising initializing the forest layer of the DEF model utilizing a gradient boosting machine. 19. The computer-readable storage medium of claim 16 jointly optimizing the parameters of both the embedding layer and the forest layer of the DEF model after fully initializing the DEF model as a result of initializing the embedding and stacking layers of the DEF model and initializing the forest layer of the DEF model utilizing the gradient boosting machine. 20. The computer-readable storage medium of claim 16 wherein accessing a DNN model comprises accessing a deep crossing model and the deep layers are multiple residual units.